Journal articles: 'Genetic Algorithm Heuristic'

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E. Nugraheni, Cecilia, Luciana Abednego, and Maria Widyarini. "A Combination of Palmer Algorithm and Gupta Algorithm for Scheduling Problem in Apparel Industry." International Journal of Fuzzy Logic Systems 11, no. 1 (January 31, 2021): 1–12. http://dx.doi.org/10.5121/ijfls.2021.11101.

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The apparel industry is a class of textile industry. Generally, the production scheduling problem in the apparel industry belongs to Flow Shop Scheduling Problems (FSSP). There are many algorithms/techniques/heuristics for solving FSSP. Two of them are the Palmer Algorithm and the Gupta Algorithm. Hyper-heuristic is a class of heuristics that enables to combine of some heuristics to produce a new heuristic. GPHH is a hyper-heuristic that is based on genetic programming that is proposed to solve FSSP [1]. This paper presents the development of a computer program that implements the GPHH. Some experiments have been conducted for measuring the performance of GPHH. From the experimental results, GPHH has shown a better performance than the Palmer Algorithm and Gupta Algorithm.

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Lie, Luo. "Heuristic Artificial Intelligent Algorithm for Genetic Algorithm." Key Engineering Materials 439-440 (June 2010): 516–21. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.516.

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A genetic algorithm is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.

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Chen, James C., Chien Wei Wu, Tran Dinh Duy Thao, Ling Huey Su, Wen Haiung Hsieh, and Tiffany Chen. "Hybrid Genetic Algorithm for Solving Assembly Line Balancing Problem in Footwear Industry." Advanced Materials Research 939 (May 2014): 623–29. http://dx.doi.org/10.4028/www.scientific.net/amr.939.623.

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This research develops a heuristic algorithm for assembly line balancing problem (ALBP) of stitching lines in footwear industry. The proposed algorithm can help to design the stitching line with workstations, machines and operators for the production of every new product model. Rank-positional-weighted heuristics and hybrid genetic algorithms are proposed to solve ALBP. First, the heuristics assign tasks and machines to workstations. This solution is then used as an initiative population for hybrid genetic algorithm for further improvement. Real data from footwear manufacturers and experimental designs are used to verify the performance of the proposed algorithm, comparing with one existing bidirectional heuristic. Results indicate that when the size and shape of shoes increase, the proposed genetic algorithm achieves better solution quality than existing heuristics.Production managers can use the research results to quickly design stitching lines for short production cycle time and high labor utilization.

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Nurserik, D., F. R. Gusmanova, G. А. Abdulkarimova, and K. S. Dalbekova. "OVERVIEW OF HEURISTIC AND METAHEURISTIC ALGORITHMS." BULLETIN Series of Physics & Mathematical Sciences 71, no. 3 (September 30, 2020): 242–47. http://dx.doi.org/10.51889/2020-3.1728-7901.37.

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The article discusses the use of heuristic algorithms for optimization problems. The algorithms for stochastic optimization are described, which constitute the main properties of the metaheuristic and its classes. Evolutionary algorithms are described in general terms. In particular, the main steps and properties of genetic algorithms are presented. The main goal of this article is to solve the vehicle routing problem using a metaheuristic algorithm. The vehicle routing problem is a complex combinatorial NP-complete optimization problem. It is shown that the metaheuristic approach to solving the problem allows one to obtain a suboptimal solution without examining the entire space of possible solutions. The genetic algorithm belongs to the group of evolutionary algorithms. The definitions are briefly given to the terms characteristic of the genetic algorithm: gene, chromosome, personality (descendant), population, descendant operators, crossing, mutation, crossover. Application of the theory of finite automata in a genetic algorithm is described. The terminology and scheme of the genetic algorithm for solving various problems are proposed.

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Kaweegitbundit, Parinya. "Comparison of Heuristic for Flow Shop Scheduling Problems with Sequence Dependent Setup Time." Advanced Materials Research 339 (September 2011): 332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.339.332.

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This paper considers flow shop scheduling problems with sequence dependent setup time. The makespan criterion has been considered. In this paper presented a comparison of three heuristics for solves this problem. The memetic algorithm, genetic algorithm and NEH heuristic have been compared. In the experimental, the result from memetic algorithm is maximum the best solution. Therefore, the MA heuristic outperforms other heuristic.

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Ardelean, Sebastian Mihai, and Mihai Udrescu. "Graph coloring using the reduced quantum genetic algorithm." PeerJ Computer Science 7 (January 3, 2022): e836. http://dx.doi.org/10.7717/peerj-cs.836.

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Genetic algorithms (GA) are computational methods for solving optimization problems inspired by natural selection. Because we can simulate the quantum circuits that implement GA in different highly configurable noise models and even run GA on actual quantum computers, we can analyze this class of heuristic methods in the quantum context for NP-hard problems. This paper proposes an instantiation of the Reduced Quantum Genetic Algorithm (RQGA) that solves the NP-hard graph coloring problem in O(N1/2). The proposed implementation solves both vertex and edge coloring and can also determine the chromatic number (i.e., the minimum number of colors required to color the graph). We examine the results, analyze the algorithm convergence, and measure the algorithm's performance using the Qiskit simulation environment. Our Reduced Quantum Genetic Algorithm (RQGA) circuit implementation and the graph coloring results show that quantum heuristics can tackle complex computational problems more efficiently than their conventional counterparts.

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Tong, Zhao, Hongjian Chen, Bilan Liu, Jinhui Cai, and Shuo Cai. "A novel intelligent hyper-heuristic algorithm for solving optimization problems." Journal of Intelligent & Fuzzy Systems 42, no. 6 (April 28, 2022): 5041–53. http://dx.doi.org/10.3233/jifs-211250.

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In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea “ML to choose heuristics” (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems.

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Tong, Zhao, Hongjian Chen, Bilan Liu, Jinhui Cai, and Shuo Cai. "A novel intelligent hyper-heuristic algorithm for solving optimization problems." Journal of Intelligent & Fuzzy Systems 42, no. 6 (April 28, 2022): 5041–53. http://dx.doi.org/10.3233/jifs-211250.

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In recent years, solving combinatorial optimization problems involves more complications, high dimensions, and multi-objective considerations. Combining the advantages of other evolutionary algorithms to enhance the performance of a unique evolutionary algorithm and form a new hybrid heuristic algorithm has become a way to strengthen the performance of the algorithm effectively. However, the intelligent hybrid heuristic algorithm destroys the integrity, universality, and robustness of the original algorithm to a certain extent and increases its time complexity. This paper implements a new idea “ML to choose heuristics” (a heuristic algorithm combined with machine learning technology) which uses the Q-learning method to learn different strategies in genetic algorithm. Moreover, a selection-based hyper-heuristic algorithm is obtained that can guide the algorithm to make decisions at different time nodes to select appropriate strategies. The algorithm is the hybrid strategy using Q-learning on StudGA (HSQ-StudGA). The experimental results show that among the 14 standard test functions, the evolutionary algorithm guided by Q-learning can effectively improve the quality of arithmetic solution. Under the premise of not changing the evolutionary structure of the algorithm, the hyper-heuristic algorithm represents a new method to solve combinatorial optimization problems.

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Chen, James C., Wun Hao Jaong, Cheng Ju Sun, Hung Yu Lee, Jenn Sheng Wu, and Chung Chao Ku. "Applying Genetic Algorithm to Resource Constrained Multi-Project Scheduling Problems." Key Engineering Materials 419-420 (October 2009): 633–36. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.633.

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Resource-constrained multi-project scheduling problems (RCMPSP) consider precedence relationship among activities and the capacity constraints of multiple resources for multiple projects. RCMPSP are NP-hard due to these practical constraints indicating an exponential calculation time to reach optimal solution. In order to improve the speed and the performance of problem solving, heuristic approaches are widely applied to solve RCMPSP. This research proposes Hybrid Genetic Algorithm (HGA) and heuristic approach to solve RCMPSP with an objective to minimize the total tardiness. HGA is compared with three typical heuristics for RCMPSP: Maximum Total Work Content, Earliest Due Date, and Minimum Slack. Two typical RCMPSP from literature are used as a test bed for performance evaluation. The results demonstrate that HGA outperforms the three heuristic methods in term of the total tardiness.

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Noshadi, Tayebe, Marzieh Dadvar, Nastaran Mirza, and Shima Shamseddini. "Adjust genetic algorithm parameter by fuzzy system." Ciência e Natura 37 (December 19, 2015): 190. http://dx.doi.org/10.5902/2179460x20771.

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Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.

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Ko, Jung-Woon, and Dong-Yeop Lee. "Path-finding Algorithm using Heuristic-based Genetic Algorithm." Journal of Korea Game Society 17, no. 5 (October 31, 2017): 123–32. http://dx.doi.org/10.7583/jkgs.2017.17.5.123.

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Rozario, Victor Stany Rozario, and Partha Sutradhar. "In-Depth Case Study on Artificial Neural Network Weights Optimization Using Meta-Heuristic and Heuristic Algorithmic Approach." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (November 23, 2022): 98–109. http://dx.doi.org/10.53799/ajse.v21i2.379.

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The Meta-heuristic and Heuristic algorithms that have been introduced for deep neural network optimization is in this paper. Artificial Intelligence, and also the most used Deep Learning methods are all growing in popularity these days, thus we need faster optimization strategies for finding the results of future activities. Neural Network Optimization with Particle Swarm Optimization, Backpropagation (BP), Resilient Propagation (Rprop), and Genetic Algorithm (GA) is used for numerical analysis of different datasets and comparing each other to find out which algorithms work better for finding optimal solutions by reducing training loss. Genetic algorithm and also bio-inspired Particle Swarm Optimization is introduced in this paper. Besides, Resilient Propagation and Conventional Backpropagation algorithms which are application-specific algorithms have also been introduced. Meta-heuristic algorithms GA and PSO are a higher-level formula and problem-independent technique that may be used to a diverse number of challenges. The characteristic of Heuristic algorithms has extremely specific features that vary depending on the problem. The conventional Backpropagation (BP) based optimization, the Particle Swarm Optimization methodology, and Resilient Propagation (Rprop) are all fully presented, and how to apply these procedures in Artificial Deep Neural networks Optimization is also thoroughly described. Applied numerical simulation over several datasets proves that the Meta-heuristic algorithm Particle Swarm Optimization and also Genetic Algorithm performs better than the conventional heuristic algorithm like Backpropagation and Resilient Propagation.

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Johns, Matthew B., Edward Keedwell, and Dragan Savic. "Adaptive locally constrained genetic algorithm for least-cost water distribution network design." Journal of Hydroinformatics 16, no. 2 (November 29, 2013): 288–301. http://dx.doi.org/10.2166/hydro.2013.218.

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This paper describes the development of an adaptive locally constrained genetic algorithm (ALCO-GA) and its application to the problem of least cost water distribution network design. Genetic algorithms have been used widely for the optimisation of both theoretical and real-world nonlinear optimisation problems, including water system design and maintenance problems. In this work we propose a heuristic-based approach to the mutation of chromosomes with the algorithm employing an adaptive mutation operator which utilises hydraulic head information and an elementary heuristic to increase the efficiency of the algorithm's search into the feasible solution space. In almost all test instances ALCO-GA displays faster convergence and reaches the feasible solution space faster than the standard genetic algorithm. ALCO-GA also achieves high optimality when compared to solutions from the literature and often obtains better solutions than the standard genetic algorithm.

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Zarea Fazlelahi, Forough, Mehrdokht Pournader, Mohsen Gharakhani, and Seyed Jafar Sadjadi. "A robust approach to design a single facility layout plan in dynamic manufacturing environments using a permutation-based genetic algorithm." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 230, no. 12 (August 8, 2016): 2264–74. http://dx.doi.org/10.1177/0954405415615728.

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During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

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Silva Arantes, Jesimar da, Márcio da Silva Arantes, Claudio Fabiano Motta Toledo, Onofre Trindade Júnior, and Brian Charles Williams. "Heuristic and Genetic Algorithm Approaches for UAV Path Planning under Critical Situation." International Journal on Artificial Intelligence Tools 26, no. 01 (February 2017): 1760008. http://dx.doi.org/10.1142/s0218213017600089.

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The present paper applies a heuristic and genetic algorithms approaches to the path planning problem for Unmanned Aerial Vehicles (UAVs), during an emergency landing, without putting at risk people and properties. The path re-planning can be caused by critical situations such as equipment failures or extreme environmental events, which lead the current UAV mission to be aborted by executing an emergency landing. This path planning problem is introduced through a mathematical formulation, where all problem constraints are properly described. Planner algorithms must define a new path to land the UAV following problem constraints. Three path planning approaches are introduced: greedy heuristic, genetic algorithm and multi-population genetic algorithm. The greedy heuristic aims at quickly find feasible paths, while the genetic algorithms are able to return better quality solutions within a reasonable computational time. These methods are evaluated over a large set of scenarios with different levels of diffculty. Simulations are also conducted by using FlightGear simulator, where the UAV’s behaviour is evaluated for different wind velocities and wind directions. Statistical analysis reveal that combining the greedy heuristic with the genetic algorithms is a good strategy for this problem.

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Yiu, Ying Fung, Jing Du, and Rabi Mahapatra. "Evolutionary Heuristic A* Search: Pathfinding Algorithm with Self-Designed and Optimized Heuristic Function." International Journal of Semantic Computing 13, no. 01 (March 2019): 5–23. http://dx.doi.org/10.1142/s1793351x19400014.

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The performance and efficiency of A* search algorithm heavily depends on the quality of the heuristic function. Therefore, designing an optimal heuristic function becomes the primary goal of developing a search algorithm for specific domains in artificial intelligence. However, it is difficult to design a well-constructed heuristic function without careful consideration and trial-and-error, especially for complex pathfinding problems. The complexity of a heuristic function increases and becomes unmanageable to design when an increasing number of parameters are involved. Existing approaches often avoid complex heuristic function design: they either trade-off the accuracy for faster computation or taking advantage of the parallelism for better performance. The objective of this paper is to reduce the difficulty of complex heuristic function design for A* search algorithm. We aim to design an algorithm that can be automatically optimized to achieve rapid search with high accuracy and low computational cost. In this paper, we present a novel design and optimization method for a Multi-Weighted-Heuristics function (MWH) named Evolutionary Heuristic A* search (EHA*) to: (1) minimize the effort on heuristic function design via Genetic Algorithm (GA), (2) optimize the performance of A* search and its variants including but not limited to WA* and MHA*, and (3) guarantee the completeness and optimality. EHA* algorithm enables high performance searches and significantly simplifies the processing of heuristic design. We apply EHA* to multiple grid-based pathfinding benchmarks to evaluate the performance. Our experiment result shows that EHA* (1) is capable of choosing an accurate heuristic function that provides an optimal solution, (2) can identify and eliminate inefficient heuristics, (3) is able to automatically design multi-heuristics function, and (4) minimizes both the time and space complexity.

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Tian, Li, Qiang Qiang Wang, and An Zhao Cao. "Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm." Applied Mechanics and Materials 291-294 (February 2013): 2164–68. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.2164.

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With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

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Polupanova, Е. Е., and P. E. Usov. "HEURISTIC GENETIC ALGORITHM FOR DIOPHANTINE EQUATIONS SOLVING." IZVESTIYA SFedU. ENGINEERING SCIENCES, no. 6 (January 31, 2022): 115–23. http://dx.doi.org/10.18522/2311-3103-2021-6-115-123.

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Hussein, Ahmed Kawther. "Hybrid Based Selective Genetic Algorithm." International Journal of Engineering Research and Advanced Technology 08, no. 03 (2022): 01–06. http://dx.doi.org/10.31695/ijerat.2022.8.3.1.

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Wireless Sensor Network WSN deployment is an active research area. Its goal is to deploy sensors in certain environment efficiently to optimize some evaluation measures. Meta heuristic searching optimization approaches have been proven to be effective in solving WSN deployment problem. They have strong power in exploring the solution space and converging toward theoptimal region. One key factor in achieving more exploring power in the meta-heuristic searching is the selection criteria of the elite solutions from one iteration to another. Two common selection criteria are roulette wheel and pairwise tournament. In this article, a hybrid based selection is applied under genetic algorithm for solving the problem of WSND. The hybrid based selection selects between roulette wheel and pairwise tournament in order to maintain good exploration and fast convergence toward the best solutions

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Sheta, Alaa F., Hossam Faris, and Ibrahim Aljarah. "Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms." International Journal of Engineering & Technology 7, no. 3.10 (July 19, 2018): 187. http://dx.doi.org/10.14419/ijet.v7i3.10.14357.

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This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local optimal. A comparison between Meta-heuristic algorithms: Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Accelerated PSO, Cuckoo Search, Krill Herd and Firefly algorithm is provided to handle the parameter estimation problem for a Winding process in the industry. The developed ARMA-meta-heuristics models for a winding machine are evaluated based on different evaluation metrics. The results reveal that meta-heuristics can provide an outstanding modeling performance.

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Jafarzadeh, H., N. Moradinasab, and M. Elyasi. "An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem." Engineering, Technology & Applied Science Research 7, no. 6 (December 18, 2017): 2260–65. http://dx.doi.org/10.48084/etasr.1570.

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The generalized traveling salesman problem (GTSP) deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS) are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

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Wiryawan, I. Made Adhi, Maria Veronica Astrid Wahyuningtyas, Anugerah Galang Persada, and Dyonisius Dony Ariananda. "A Microstrip Antenna Design Using an Heuristic Algorithm." IJITEE (International Journal of Information Technology and Electrical Engineering) 4, no. 1 (September 9, 2020): 25. http://dx.doi.org/10.22146/ijitee.56343.

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Microstrip antennas have several advantages. Some of them are that they have a compact shape and small dimensions. Moreover, they are also easy to be fabricated and easily connected as well as integrated with other electronic devices. Currently, designing antennas conventionally is limited by time, energy, and experience as well as expertise. As an alternative, a way to design antennas with revolutionary methods is developed using algorithms and computing. Algorithm design techniques can overcome limitations and automatically find practical solutions that usually take a long time to discover. The particle swarm optimization algorithm and a genetic algorithm can find solutions from microstrip antennas. Objective functions play an essential role in heuristic algorithms. With a proper objective function, simulation results are obtained on the particle swarm optimization algorithm with a return loss value of -47.837, VSWR of 1.0083, and impedance of 46.805 Ω. In contrast, the genetic algorithm obtains return loss of -16.157 dB, impedance of 50.233 Ω, and VSWR of 1.3687.

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Burke, Edmund K., Matthew R. Hyde, Graham Kendall, and John Woodward. "Automating the Packing Heuristic Design Process with Genetic Programming." Evolutionary Computation 20, no. 1 (March 2012): 63–89. http://dx.doi.org/10.1162/evco_a_00044.

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The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.

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Bulitko, Vadim, Shuwei Wang, Justin Stevens, and Levi H. S. Lelis. "Portability and Explainability of Synthesized Formula-based Heuristics." Proceedings of the International Symposium on Combinatorial Search 15, no. 1 (July 17, 2022): 29–37. http://dx.doi.org/10.1609/socs.v15i1.21749.

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Heuristic search is a key component of automated planning and pathfinding. It is guided by a heuristic function which estimates remaining solution cost. Traditionally heuristic functions for pathfinding have been human-designed or pre-computed for a specific search graph. The former tend to be compact, human-readable but generic. The latter offer better guidance but require per-graph pre-computation and have a substantial memory cost. We aim to retain compactness and readability of human-designed heuristics and increase their performance. We adopt the recently published approach of representing heuristic functions as algebraic formulae and automatically synthesizing them for video-game maps. Whereas published work merely randomly sampled the space of formula-based heuristic functions, we implement and evaluate a parameterized synthesis algorithm that unifies and generalizes the stochastic sampling, simulated annealing and a basic genetic algorithm. We tune the parameters for better synthesis performance and then, using maps from multiple video games, show that heuristics synthesized for maps from one game still outperform the baseline search (A* with weighted Manhattan distance) on maps from a different game. We analyze a frequently synthesized formula and explain how, despite having a higher error than the Manhattan distance, it takes advantage of the structure in video-game pathfinding problems and speeds up A*.

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Vose, Michael D. "Logarithmic Convergence of Random Heuristic Search." Evolutionary Computation 4, no. 4 (December 1996): 395–404. http://dx.doi.org/10.1162/evco.1996.4.4.395.

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This paper speaks to the inherent emergent behavior of genetic search. For completeness and generality, a class of stochastic search algorithms, random heuristic search, is reviewed. A general convergence theorem for this class is then proved. Since the simple genetic algorithm (GA) is an instance of random heuristic search, a corollary is a result concerning GAs and time to convergence.

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Arıcı, FerdaNur, and Ersin Kaya. "Comparison of Meta-heuristic Algorithms on Benchmark Functions." Academic Perspective Procedia 2, no. 3 (November 22, 2019): 508–17. http://dx.doi.org/10.33793/acperpro.02.03.41.

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Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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Yin, Lu, Junlin Qiu, and Shangbing Gao. "Biclustering of Gene Expression Data Using Cuckoo Search and Genetic Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11 (July 24, 2018): 1850039. http://dx.doi.org/10.1142/s0218001418500398.

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Biclustering analysis of gene expression data can reveal a large number of biologically significant local gene expression patterns. Therefore, a large number of biclustering algorithms apply meta-heuristic algorithms such as genetic algorithm (GA) and cuckoo search (CS) to analyze the biclusters. However, different meta-heuristic algorithms have different applicability and characteristics. For example, the CS algorithm can obtain high-quality bicluster and strong global search ability, but its local search ability is relatively poor. In contrast to the CS algorithm, the GA has strong local search ability, but its global search ability is poor. In order to not only improve the global search ability of a bicluster and its coverage, but also improve the local search ability of the bicluster and its quality, this paper proposed a meta-heuristic algorithm based on GA and CS algorithm (GA-CS Biclustering, Georgia Association of Community Service Boards (GACSB)) to solve the problem of gene expression data clustering. The algorithm uses the CS algorithm as the main framework, and uses the tournament strategy and the elite retention strategy based on the GA to generate the next generation of the population. Compared with the experimental results of common biclustering analysis algorithms such as correlated correspondence (CC), fast, local clustering (FLOC), interior search algorithm (ISA), Securities Exchange Board of India (SEBI), sum of squares between (SSB) and coordinated scheduling/beamforming (CSB), the GACSB algorithm can not only obtain biclusters of high quality, but also obtain biclusters of high-biologic significance. In addition, we also use different bicluster evaluation indicators, such as Average Correlation Value (ACV), Mean-Squared Residue (MSR) and Virtual Error (VE), and verify that the GACSB algorithm has a strong scalability.

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Korkmaz Tan, Rabia, and Şebnem Bora. "Adaptive parameter tuning for agent-based modeling and simulation." SIMULATION 95, no. 9 (June 25, 2019): 771–96. http://dx.doi.org/10.1177/0037549719846366.

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The purpose of this study was to solve the parameter-tuning problem of complex systems modeled in an agent-based modeling and simulation environment. As a good set of parameters is necessary to demonstrate the target behavior in a realistic way, modeling a complex system constitutes an optimization problem that must be solved for systems with large parameter spaces. This study presents a three-step hybrid parameter-tuning approach for agent-based models and simulations. In the first step, the problem is defined; in the second step, a parameter-tuning process is performed using the following meta-heuristic algorithms: the Genetic Algorithm, the Firefly Algorithm, the Particle Swarm Optimization algorithm, and the Artificial Bee Colony algorithm. The critical parameters of the meta-heuristic algorithms used in the second step are tuned using the adaptive parameter-tuning method. Thus, new meta-heuristic algorithms are developed, namely, the Adaptive Genetic Algorithm, the Adaptive Firefly Algorithm, the Adaptive Particle Swarm Optimization algorithm, and the Adaptive Artificial Bee Colony algorithm. In the third step, the control phase, the algorithm parameters obtained via the adaptive parameter-tuning method and the parameter values of the model obtained from the meta-heuristic algorithms are manually provided to the developed tool performing the parameter-tuning process and they are tested. The best results are achieved when the meta-heuristic algorithms that were successful in the optimization process are used with their critical parameters adjusted for optimum results. The proposed approach is tested by using the Predator–Prey model, the Eight Queens model, and the Flow Zombies model, and the results are compared.

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Wang, Yong. "The Genetic Algorithm with Two Heuristic Rules for TSP." Advanced Materials Research 694-697 (May 2013): 2787–93. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2787.

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Many complex discrete manufacturing problems, such as manufacturing sequencing problem or machine scheduling problem etc, can be converted into a general traveling salesman problem (TSP). TSP has been proven to be NP-complete. The genetic algorithm is improved with two heuristic rules for TSP. The first heuristic rule is the four vertices and three lines inequality. It is applied to the local Hamiltonian paths to generate the better solutions. The second heuristic rule is executed to reverse the local Hamiltonian paths, which generates new better solutions. The two heuristic rules coordinate with each other and they are merged into the optimization process of genetic algorithm to improve its performance. The computation results show that the improved genetic algorithm can find the near optimal solutions for most of the TSP instances.

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El Hassani, Hicham, Said Benkachcha, and Jamal Benhra. "New Genetic Operator (Jump Crossover) for the Traveling Salesman Problem." International Journal of Applied Metaheuristic Computing 6, no. 2 (April 2015): 33–44. http://dx.doi.org/10.4018/ijamc.2015040103.

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Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. The authors adopt a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

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EZZIANE, ZOHEIR. "Solving the 0/1 knapsack problem using an adaptive genetic algorithm." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, no. 1 (January 2002): 23–30. http://dx.doi.org/10.1017/s0890060401020030.

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Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

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Qin, Lei, Ya Qin Li, and Kang Zhou. "Vehicle Routing Problem Based on Heuristic Artificial Fish School Algorithm." Applied Mechanics and Materials 721 (December 2014): 56–61. http://dx.doi.org/10.4028/www.scientific.net/amm.721.56.

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Vehicle Routing Problem (VRP) is one of the core issue of logistics distribution, for traditional precision algorithms and heuristic algorithms had low accuracies or easily fell into local optimal solutions, it was difficult to obtain the optimal solution. This paper proposes a heuristic artificial fish school algorithm (HAFSA) for VRP, firstly, three-dimensional particle coding method is applied to vehicle routing code, and infeasible and inadequate artificial fish coding for heuristic repair, secondly HAFSA steps are given, finally the algorithm is tested through a simulative example. The experimental results show that compared with traditional genetic algorithm (GA) and particle swarm optimization (PSO), AFSA and their extension algorithms, HAFSA has a better performance in time and space cost and convergence.

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Li, Ang, and Jin Yun Pu. "Damaged Ship Anti-Flooding Decision Plan Intelligent Generation System Based on Petri Net and Heuristic Color Genetic Algorithm." Advanced Materials Research 706-708 (June 2013): 1866–70. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1866.

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No matter in the wartime or in the peace time, the intelligent generation system of damaged ship anti-flooding decision plan is an important tool to guarantee ship survivability and safety. The intelligent decision plan generation system which has high search efficiency plays an important role in recovering the buoyancy and stability indicts of damaged ship. The intelligent decision plan generation system introduced in this paper contains Petri net model and heuristic color genetic algorithm. The Petri net is used to model the ship anti-flooding decision process and the heuristic color genetic algorithm is used to solve intelligent hull balance decision problem. The traditional genetic algorithm is improved according to the special demand of hull balance. Based on the definition of the colored gene and the foundation of the heuristic search rules, the heuristic color genetic algorithm is given to improve the traditional genetic algorithm search efficiency.

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Domović, Daniel, Tomislav Rolich, and Marin Golub. "Hyper-Heuristic Approach for Improving Marker Efficiency." Autex Research Journal 18, no. 4 (December 1, 2018): 348–63. http://dx.doi.org/10.1515/aut-2018-0026.

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Abstract Marker planning is an optimization arrangement problem, where a set of cutting parts need to be placed on a thin paper without overlapping to create a marker – an exact diagram of cutting parts that will be cut from a single spread. An optimal marker that utilizes the length of textile material has to be obtained. The aim of this research was to develop novel algorithms for obtaining an efficient marker that would achieve competitive results and optimize the garment production in terms of improving the utilization of textile material. In this research, a novel Grid heuristic was introduced for obtaining a marker, alongside its improvement methods: Grid-BLP and Grid-Shaking. These heuristics were hybridized with genetic algorithm that determined the placement order of cutting parts using the newly introduced All Equal First (AEF) placement order. A novel individual representation for genetic algorithm was designed that was composed of order sequence, rotation detection and the choice of placement algorithm (hyper-heuristic). Experiments were conducted to determine the best marker making method, and hyper-heuristic efficiency. The implementation and experiments were conducted in MATLAB using GEATbx toolbox on five datasets from the garment industry: ALBANO, DAGLI, MAO, MARQUES and MAN SHIRT. Marker efficiency in percentage was recorded with best results: 84.50%, 80.13%, 79.54%, 84.67% and 86.02% obtained for the datasets respectively. The most efficient heuristic was Grid-Shaking. Hyper-heuristic applied Grid-Shaking in 88% of times. The created algorithm is independent of cutting parts’ shape. It can produce markers of arbitrary shape and is flexible in terms of expansion to new instances from the garment industry (leather nesting, avoiding damaged areas of material, marker making with materials with patterns).

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Kazakovtsev, Lev, Ivan Rozhnov, Guzel Shkaberina, and Viktor Orlov. "K-Means Genetic Algorithms with Greedy Genetic Operators." Mathematical Problems in Engineering 2020 (November 27, 2020): 1–16. http://dx.doi.org/10.1155/2020/8839763.

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The k-means problem is one of the most popular models of cluster analysis. The problem is NP-hard, and modern literature offers many competing heuristic approaches. Sometimes practical problems require obtaining such a result (albeit notExact), within the framework of the k-means model, which would be difficult to improve by known methods without a significant increase in the computation time or computational resources. In such cases, genetic algorithms with greedy agglomerative heuristic crossover operator might be a good choice. However, their computational complexity makes it difficult to use them for large-scale problems. The crossover operator which includes the k-means procedure, taking the absolute majority of the computation time, is essential for such algorithms, and other genetic operators such as mutation are usually eliminated or simplified. The importance of maintaining the population diversity, in particular, with the use of a mutation operator, is more significant with an increase in the data volume and available computing resources such as graphical processing units (GPUs). In this article, we propose a new greedy heuristic mutation operator for such algorithms and investigate the influence of new and well-known mutation operators on the objective function value achieved by the genetic algorithms for large-scale k-means problems. Our computational experiments demonstrate the ability of the new mutation operator, as well as the mechanism for organizing subpopulations, to improve the result of the algorithm.

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Agarwal, Mohit, and Gur Mauj Saran Srivastava. "Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment." International Journal of Information Technology & Decision Making 17, no. 04 (July 2018): 1237–67. http://dx.doi.org/10.1142/s0219622018500244.

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Task scheduling is one of the most difficult problems which is associated with cloud computing. Due to its nature, as it belongs to nondeterministic polynomial time (NP)-hard class of problem. Various heuristic as well as meta-heuristic approaches have been used to find the optimal solution. Task scheduling basically deals with the allocation of the task to the most efficient machine for optimal utilization of the computing resources and results in better makespan. As per literature, various meta-heuristic algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and their other hybrid techniques have been applied. Through this paper, we are presenting a novel meta-heuristic technique — genetic algorithm enabled particle swarm optimization (PSOGA), a hybrid version of PSO and GA algorithm. PSOGA uses the diversification property of PSO and intensification property of the GA. The proposed algorithm shows its supremacy over other techniques which are taken into consideration by presenting less makespan time in majority of the cases which leads up to 22.2% improvement in performance of the system and also establishes that proposed PSOGA algorithm converges faster than the others.

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Zhao, Xibin, Hehua Zhang, Yu Jiang, Songzheng Song, Xun Jiao, and Ming Gu. "An Effective Heuristic-Based Approach for Partitioning." Journal of Applied Mathematics 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/138037.

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As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.

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Aivaliotis-Apostolopoulos, Panagiotis, and Dimitrios Loukidis. "Swarming genetic algorithm: A nested fully coupled hybrid of genetic algorithm and particle swarm optimization." PLOS ONE 17, no. 9 (September 23, 2022): e0275094. http://dx.doi.org/10.1371/journal.pone.0275094.

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Particle swarm optimization and genetic algorithms are two classes of popular heuristic algorithms that are frequently used for solving complex multi-dimensional mathematical optimization problems, each one with its one advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration, and as a result it often converges rapidly to local optima other than the global optimum. The genetic algorithm has the ability to overcome local extrema throughout the optimization process, but it often suffers from slow convergence rates. This paper proposes a new hybrid algorithm that nests particle swarm optimization operations in the genetic algorithm, providing the general population with the exploitation prowess of the genetic algorithm and a sub-population with the high exploitation capabilities of particle swarm optimization. The effectiveness of the proposed algorithm is demonstrated through solutions of several continuous optimization problems, as well as discrete (traveling salesman) problems. It is found that the new hybrid algorithm provides a better balance between exploration and exploitation compared to both parent algorithms, as well as existing hybrid algorithms, achieving consistently accurate results with relatively small computational cost.

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Abdul-Niby, M., M. Alameen, A. Salhieh, and A. Radhi. "Improved Genetic and Simulating Annealing Algorithms to Solve the Traveling Salesman Problem Using Constraint Programming." Engineering, Technology & Applied Science Research 6, no. 2 (April 17, 2016): 927–30. http://dx.doi.org/10.48084/etasr.627.

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The Traveling Salesman Problem (TSP) is an integer programming problem that falls into the category of NP-Hard problems. As the problem become larger, there is no guarantee that optimal tours will be found within reasonable computation time. Heuristics techniques, like genetic algorithm and simulating annealing, can solve TSP instances with different levels of accuracy. Choosing which algorithm to use in order to get a best solution is still considered as a hard choice. This paper suggests domain reduction as a tool to be combined with any meta-heuristic so that the obtained results will be almost the same. The hybrid approach of combining domain reduction with any meta-heuristic encountered the challenge of choosing an algorithm that matches the TSP instance in order to get the best results.

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Abdul-Razaq, Tariq, Hanan Chachan, and Faez Ali. "Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 19, 2021): 1–20. http://dx.doi.org/10.55562/jrucs.v30i2.361.

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The NP-completeness of flow shops scheduling problems has been discussed for many years. Hence many heuristics have been proposed to obtain solutions of good quality with a small computational effort. The CDS (Campbell et al) and NEH (Nawaz, Enscore and Ham) heuristics are efficient among meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).This paper discusses some methods and suggests new developing to the methods of the scheduling in flow shop to minimize makespan problems. Our main object in this paper, from one side, is to improve efficient heuristics which will be better than the existing heuristics given in the literature and yield solutions within a short time like Simple Heuristic Methods (SHM) and the First Heuristic Decreasing Arrange (DR). From other side, we apply two local search methods like GA and PSO algorithms on flow shop problems.Experimental analysis has been given of the performance of the proposed heuristics and local search methods with the relative efficient existing heuristics.

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Reynolds, B. J., and S. Azarm. "A MULTI-OBJECTIVE HEURISTIC-BASED HYBRID GENETIC ALGORITHM*." Mechanics of Structures and Machines 30, no. 4 (January 12, 2002): 463–91. http://dx.doi.org/10.1081/sme-120015073.

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Reynolds, B. J., and S. Azarm. "A Multi-objective Heuristic-Based Hybrid Genetic Algorithm." Mechanics Based Design of Structures and Machines 31, no. 1 (January 4, 2003): 125. http://dx.doi.org/10.1081/sme-120020384.

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Jiao, Jianxin (Roger), Yiyang Zhang, and Yi Wang. "A heuristic genetic algorithm for product portfolio planning." Computers & Operations Research 34, no. 6 (June 2007): 1777–99. http://dx.doi.org/10.1016/j.cor.2005.05.033.

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Singh, Richa, Suraiya Parveen, and Aparna . "Meta Heuristic Optimization of TSP using Genetic Algorithm." International Journal of Advance Research and Innovation 2, no. 1 (2014): 42–45. http://dx.doi.org/10.51976/ijari.211406.

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Initiation of this paper demarcates the limits of artificial intelligence, as it calls artificial intelligence a science for its extensibility and genetic algorithm to provide an affable decision to solve a popular routing problem named as Travelling Salesman Problem. This study will help more in moving to a world where a computer will be able to program based on natural selection and evolution. The travelling salesperson (or, salesman) problem (TSP) is an important combinatorial optimization problem. A combinatorial optimization problem helps to find an optimal object from a finite set of objects which does not follows an exhaustive search but the set of feasible solution is discrete. TSP aims to find the shortest path that travels through every city in a provided set of cities exactly once and travels back to the initial city using genetic algorithm. TSP is complex as it is a NP-complete problem. This literature proposes a heuristic approach for solving TSP: To find shortest path that travels through every city in a provided set of cities exactly once and travels back to the initial city. Proposed solution will provide a faster solution but not necessarily an optimal solution.

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Barukčić, Marinko, Srete Nikolovski, and Franjo Jović. "Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation." Journal of Electrical Engineering 61, no. 6 (November 1, 2010): 332–40. http://dx.doi.org/10.2478/v10187-011-0052-1.

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Hybrid Evolutionary-Heuristic Algorithm for Capacitor Banks Allocation The issue of optimal allocation of capacitor banks concerning power losses minimization in distribution networks are considered in this paper. This optimization problem has been recently tackled by application of contemporary soft computing methods such as: genetic algorithms, neural networks, fuzzy logic, simulated annealing, ant colony methods, and hybrid methods. An evolutionaryheuristic method has been proposed for optimal capacitor allocation in radial distribution networks. An evolutionary method based on genetic algorithm is developed. The proposed method has a reduced number of parameters compared to the usual genetic algorithm. A heuristic stage is used for improving the optimal solution given by the evolutionary stage. A new cost-voltage node index is used in the heuristic stage in order to improve the quality of solution. The efficiency of the proposed two-stage method has been tested on different test networks. The quality of solution has been verified by comparison tests with other methods on the same test networks. The proposed method has given significantly better solutions for time dependent load in the 69-bus network than found in references.

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Bothra, Sandeep Kumar, Sunita Singhal, and Hemlata Goyal. "Deadline-Constrained Cost-Effective Load-Balanced Improved Genetic Algorithm for Workflow Scheduling." International Journal of Information Technology and Web Engineering 16, no. 4 (October 2021): 1–34. http://dx.doi.org/10.4018/ijitwe.2021100101.

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Resource scheduling in a cloud computing environment is noteworthy for scientific workflow execution under a cost-effective deadline constraint. Although various researchers have proposed to resolve this critical issue by applying various meta-heuristic and heuristic approaches, no one is able to meet the strict deadline conditions with load-balanced among machines. This article has proposed an improved genetic algorithm that initializes the population with a greedy strategy. Greedy strategy assigns the task to a virtual machine that is under loaded instead of assigning the tasks randomly to a machine. In general workflow scheduling, task dependency is tested after each crossover and mutation operators of genetic algorithm, but here the authors perform after the mutation operation only which yield better results. The proposed model also considered booting time and performance variation of virtual machines. The authors compared the algorithm with previously developed heuristics and metaheuristics both and found it increases hit rate and load balance. It also reduces execution time and cost.

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Xiang, Xianbo, Caoyang Yu, He Xu, and Stuart X. Zhu. "Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm." Complexity 2018 (November 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/2024184.

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This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.

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Jakus, Damir, Rade Čađenović, Josip Vasilj, and Petar Sarajčev. "Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm." Energies 13, no. 7 (March 26, 2020): 1544. http://dx.doi.org/10.3390/en13071544.

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This paper describes the algorithm for optimal distribution network reconfiguration using the combination of a heuristic approach and genetic algorithms. Although similar approaches have been developed so far, they usually had issues with poor convergence rate and long computational time, and were often applicable only to the small scale distribution networks. Unlike these approaches, the algorithm described in this paper brings a number of uniqueness and improvements that allow its application to the distribution networks of real size with a high degree of topology complexity. The optimal distribution network reconfiguration is formulated for the two different objective functions: minimization of total power/energy losses and minimization of network loading index. In doing so, the algorithm maintains the radial structure of the distribution network through the entire process and assures the fulfilment of various physical and operational network constraints. With a few minor modifications in the heuristic part of the algorithm, it can be adapted to the problem of determining the distribution network optimal structure in order to equalize the network voltage profile. The proposed algorithm was applied to a variety of standard distribution network test cases, and the results show the high quality and accuracy of the proposed approach, together with a remarkably short execution time.

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Johns, Matthew B., Edward Keedwell, and Dragan Savic. "Knowledge-based multi-objective genetic algorithms for the design of water distribution networks." Journal of Hydroinformatics 22, no. 2 (November 29, 2019): 402–22. http://dx.doi.org/10.2166/hydro.2019.106.

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Abstract Water system design problems are complex and difficult to optimise. It has been demonstrated that involving engineering expertise is required to tackle real-world problems. This paper presents two engineering inspired hybrid evolutionary algorithms (EAs) for the multi-objective design of water distribution networks. The heuristics are developed from traditional design approaches of practicing engineers and integrated into the mutation operator of a multi-objective EA. The first engineering inspired heuristic is designed to identify hydraulic bottlenecks within the network and eliminate them with a view to speeding up the algorithm's search to the feasible solution space. The second heuristic is based on the notion that pipe diameters smoothly transition from large, at the source, to small at the extremities of the network. The performance of the engineering inspired hybrid EAs is compared with Non-Dominated Sorting Genetic Algorithm II and assessed on three networks of varying complexity, two benchmarks and one real-world network. The experiments presented in this paper demonstrate that the incorporation of engineering expertise can improve EA performance, often producing superior solutions both in terms of mathematical optimality and also engineering feasibility.

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Zhang, Hankun, Borut Buchmeister, Xueyan Li, and Robert Ojstersek. "Advanced Metaheuristic Method for Decision-Making in a Dynamic Job Shop Scheduling Environment." Mathematics 9, no. 8 (April 19, 2021): 909. http://dx.doi.org/10.3390/math9080909.

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As a well-known NP-hard problem, the dynamic job shop scheduling problem has significant practical value, so this paper proposes an Improved Heuristic Kalman Algorithm to solve this problem. In Improved Heuristic Kalman Algorithm, the cellular neighbor network is introduced, together with the boundary handling function, and the best position of each individual is recorded for constructing the cellular neighbor network. The encoding method is introduced based on the relative position index so that the Improved Heuristic Kalman Algorithm can be applied to solve the dynamic job shop scheduling problem. Solving the benchmark example of dynamic job shop scheduling problem and comparing it with the original Heuristic Kalman Algorithm and Genetic Algorithm-Mixed, the results show that Improved Heuristic Kalman Algorithm is effective for solving the dynamic job shop scheduling problem. The convergence rate of the Improved Heuristic Kalman Algorithm is reduced significantly, which is beneficial to avoid the algorithm from falling into the local optimum. For all 15 benchmark instances, Improved Heuristic Kalman Algorithm and Heuristic Kalman Algorithm have obtained the best solution obtained by Genetic Algorithm-Mixed. Moreover, for 9 out of 15 benchmark instances, they achieved significantly better solutions than Genetic Algorithm-Mixed. They have better robustness and reasonable running time (less than 30 s even for large size problems), which means that they are very suitable for solving the dynamic job shop scheduling problem. According to the dynamic job shop scheduling problem applicability, the integration-communication protocol was presented, which enables the transfer and use of the Improved Heuristic Kalman Algorithm optimization results in the conventional Simio simulation environment. The results of the integration-communication protocol proved the numerical and graphical matching of the optimization results and, thus, the correctness of the data transfer, ensuring high-level usability of the decision-making method in a real-world environment.

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Journal articles on the topic 'Genetic Algorithm Heuristic'

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