Статті в журналах: "Discrete Particle Swarm Optimization"

1

Roy, Rahul, Satchidananda Dehuri, and Sung Bae Cho. "A Novel Particle Swarm Optimization Algorithm for Multi-Objective Combinatorial Optimization Problem." International Journal of Applied Metaheuristic Computing 2, no. 4 (October 2011): 41–57. http://dx.doi.org/10.4018/jamc.2011100104.

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The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.

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2

Xiao, Bin, and Zhao Hui Li. "An Improved Hybrid Discrete Particle Swarm Optimization Algorithm to Solve the TSP Problem." Applied Mechanics and Materials 130-134 (October 2011): 3589–94. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.3589.

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Through investigating the issue of solving the TSP problem by discrete particle swarm optimization algorithm, this study finds a new discrete particle swarm optimization algorithm (NDPSO), which is easy to combine with other algorithm and has fast convergence and high accuracy, by introducing the thought of the greedy algorithm and GA algorithm and refining the discrete particle swarm optimization algorithm. And then the study expands NDPSO by Simulated Annealing algorithm and proposes a hybrid discrete particle swarm optimization algorithm (HDPSO). At last, the experiments prove that these two algorithms both have good convergence, but the HDPSO has a better capacity to find the best solution.

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3

Ting, T. O., H. C. Ting, and T. S. Lee. "Taguchi-Particle Swarm Optimization for Numerical Optimization." International Journal of Swarm Intelligence Research 1, no. 2 (April 2010): 18–33. http://dx.doi.org/10.4018/jsir.2010040102.

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In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

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4

Wang, Bei Zhan, Xiang Deng, Wei Chuan Ye, and Hai Fang Wei. "Study on Discrete Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 220-223 (November 2012): 1787–94. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1787.

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The particle swarm optimization (PSO) algorithm is a new type global searching method, which mostly focus on the continuous variables and little on discrete variables. The discrete forms and discretized methods have received more attention in recent years. This paper introduces the basic principles and mechanisms of PSO algorithm firstly, then points out the process of PSO algorithm and depict the operation rules of discrete PSO algorithm. Various improvements and applications of discrete PSO algorithms are reviewed. The mechanisms and characteristics of two different discretized strategies are presented. Some development trends and future research directions about discrete PSO are proposed.

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5

Kang, Qi, Lei Wang, and Qidi Wu. "Swarm-based approximate dynamic optimization process for discrete particle swarm optimization system." International Journal of Bio-Inspired Computation 1, no. 1/2 (2009): 61. http://dx.doi.org/10.1504/ijbic.2009.022774.

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6

Beheshti, Zahra, Siti Mariyam Shamsuddin, and Shafaatunnur Hasan. "Memetic binary particle swarm optimization for discrete optimization problems." Information Sciences 299 (April 2015): 58–84. http://dx.doi.org/10.1016/j.ins.2014.12.016.

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7

Sarathambekai, S., and K. Umamaheswari. "Intelligent discrete particle swarm optimization for multiprocessor task scheduling problem." Journal of Algorithms & Computational Technology 11, no. 1 (September 19, 2016): 58–67. http://dx.doi.org/10.1177/1748301816665521.

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Discrete particle swarm optimization is one of the most recently developed population-based meta-heuristic optimization algorithm in swarm intelligence that can be used in any discrete optimization problems. This article presents a discrete particle swarm optimization algorithm to efficiently schedule the tasks in the heterogeneous multiprocessor systems. All the optimization algorithms share a common algorithmic step, namely population initialization. It plays a significant role because it can affect the convergence speed and also the quality of the final solution. The random initialization is the most commonly used method in majority of the evolutionary algorithms to generate solutions in the initial population. The initial good quality solutions can facilitate the algorithm to locate the optimal solution or else it may prevent the algorithm from finding the optimal solution. Intelligence should be incorporated to generate the initial population in order to avoid the premature convergence. This article presents a discrete particle swarm optimization algorithm, which incorporates opposition-based technique to generate initial population and greedy algorithm to balance the load of the processors. Make span, flow time, and reliability cost are three different measures used to evaluate the efficiency of the proposed discrete particle swarm optimization algorithm for scheduling independent tasks in distributed systems. Computational simulations are done based on a set of benchmark instances to assess the performance of the proposed algorithm.

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8

Zhang, Jun Ting, and Li Xia Qiao. "Optimization Mechanism Control Strategy of Vehicle Routing Problem Based on Improved PSO." Advanced Materials Research 681 (April 2013): 130–36. http://dx.doi.org/10.4028/www.scientific.net/amr.681.130.

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Traveling salesman problem based on vehicle routing problem in the case, according to the discrete domain specificity, redefine the problem domain to the mapping relationship between particles and related operation rules, and the introduction of self learning operator so that the PSO algorithm can deal with discrete problem. Vehicle Routing Problem (VRP) is research on how to plan the vehicles routes in order to save the transportation cost. Improved Particle Swarm Optimization (PSO) algorithm is proposed to solve the VRP in this paper. To improve the efficiency of the Particle Swarm Optimization, self-learning operator is constructed. Particles are re coded and operate rules are redefined to deal with the discrete problem of VRP. The effectiveness of the proposed algorithm is demonstrated by the simulations.

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9

Wu, Yanmin, and Qipeng Song. "Improved Particle Swarm Optimization Algorithm in Power System Network Reconfiguration." Mathematical Problems in Engineering 2021 (March 11, 2021): 1–10. http://dx.doi.org/10.1155/2021/5574501.

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With the rapid development of the social economy, the rapid development of all social circles places higher demands on the electricity industry. As a fundamental industry supporting the salvation of the national economy, society, and human life, the electricity industry will face a significant improvement and the restructuring of the network as an important part of the power system should also be optimised. This paper first introduces the development history of swarm intelligence algorithm and related research work at home and abroad. Secondly, it puts forward the importance of particle swarm optimization algorithm for power system network reconfiguration and expounds the basic principle, essential characteristics, and basic model of the particle swarm optimization algorithm. This paper completes the work of improving PSO through the common improved methods of PSO and the introduction of mutation operation and tent mapping. In the experimental simulation part, the improved particle swarm optimization algorithm is used to simulate the 10-machine 39-bus simulation system in IEEE, and the experimental data are compared with the chaos genetic algorithm and particle swarm optimization discrete algorithm. Through the experimental data, we can know that the improved particle swarm optimization algorithm has the least number of actions in switching times, only 4 times, and the chaos genetic algorithm and discrete particle swarm optimization algorithm are 5 times; compared with the other two algorithms, the improved particle swarm optimization algorithm has the fastest convergence speed and the highest convergence accuracy. The improved particle swarm optimization algorithm proposed in this paper provides an excellent solution for power system network reconfiguration and has important research significance for power system subsequent optimization and particle swarm optimization algorithm improvement.

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10

R. B., Madhumala, Harshvardhan Tiwari, and Devaraj Verma C. "Resource Optimization in Cloud Data Centers Using Particle Swarm Optimization." International Journal of Cloud Applications and Computing 12, no. 2 (April 1, 2022): 1–12. http://dx.doi.org/10.4018/ijcac.305856.

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To meet the ever-growing demand for computational resources, it is mandatory to have the best resource allocation algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is used to address the resource optimization problem. Particle Swarm Optimization is suitable for continuous data optimization, to use in discrete data as in the case of Virtual Machine placement we need to fine-tune some of the parameters in Particle Swarm Optimization. The Virtual Machine placement problem is addressed by our proposed model called Improved Particle Swarm Optimization (IM-PSO), where the main aim is to maximize the utilization of resources in the cloud datacenter. The obtained results show that the proposed algorithm provides an optimized solution when compared to the existing algorithms.

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11

Amallynda, Ikhlasul. "The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem." Jurnal Teknik Industri 20, no. 2 (August 31, 2019): 1. http://dx.doi.org/10.22219/jtiumm.vol20.no2.1-12.

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In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms

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12

Amallynda, Ikhlasul. "The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem." Jurnal Teknik Industri 20, no. 2 (August 31, 2019): 105. http://dx.doi.org/10.22219/jtiumm.vol20.no2.105-116.

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Анотація:

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms

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13

El-Abd, Mohammed, Hassan Hassan, Mohab Anis, Mohamed S. Kamel, and Mohamed Elmasry. "Discrete cooperative particle swarm optimization for FPGA placement." Applied Soft Computing 10, no. 1 (January 2010): 284–95. http://dx.doi.org/10.1016/j.asoc.2009.07.011.

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14

Qin, Jin, Xin Li, and Yixin Yin. "An algorithmic framework of discrete particle swarm optimization." Applied Soft Computing 12, no. 3 (March 2012): 1125–30. http://dx.doi.org/10.1016/j.asoc.2011.11.012.

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15

Abdel-Kader, Rehab F. "Particle Swarm Optimization for Constrained Instruction Scheduling." VLSI Design 2008 (March 15, 2008): 1–7. http://dx.doi.org/10.1155/2008/930610.

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Instruction scheduling is an optimization phase aimed at balancing the performance-cost tradeoffs of the design of digital systems. In this paper, a formal framework is tailored in particular to find an optimal solution to the resource-constrained instruction scheduling problem in high-level synthesis. The scheduling problem is formulated as a discrete optimization problem and an efficient population-based search technique; particle swarm optimization (PSO) is incorporated for efficient pruning of the solution space. As PSO has proven to be successful in many applications in continuous optimization problems, the main contribution of this paper is to propose a new hybrid algorithm that combines PSO with the traditional list scheduling algorithm to solve the discrete problem of instruction scheduling. The performance of the proposed algorithms is evaluated on a set of HLS benchmarks, and the experimental results demonstrate that the proposed algorithm outperforms other scheduling metaheuristics and is a promising alternative for obtaining near optimal solutions to NP-complete scheduling problem instances.

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16

Hussein, Yahya, and ALI Sahan. "An Intelligent Ear Recognition Technique." International Journal of Advances in Soft Computing and its Applications 13, no. 3 (November 28, 2021): 13–27. http://dx.doi.org/10.15849/ijasca.211128.02.

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The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.

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17

Singh, Deepak, Vikas Singh, and Uzma Ansari. "Binary Particle Swarm Optimization with Crossover Operation for Discrete Optimization." International Journal of Computer Applications 28, no. 11 (August 31, 2011): 19–24. http://dx.doi.org/10.5120/3428-4281.

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18

Wu, Hua Li, Jin Hua Wu, and Ai Li Liu. "Hybrid Discrete Particle Swarm Optimizer Algorithm for Traveling Salesman Problem." Advanced Materials Research 433-440 (January 2012): 4526–29. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.4526.

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PSO has been widely used in continuous optimization problems, but in discrete domain the research and application is very little. By redefining the position and speed of particles and related operations, the discrete particle swarm algorithm can be constructed. Due to the weak capacity of local search of PSO and be easy to constringe the local optimum, it is combined with simulated annealing and the hybrid discrete PSO is constructed using the characteristics that simulated annealing can accept some ungraded solution under the control of certain probability,finally the algorithm is applied to solving the traveling salesman problem successfully. The simulation results show that the hybrid discrete PSO can get better optimization effect, which validates the effectiveness of the method.

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19

Feng, Hong Kui, Jin Song Bao, and Jin Ye. "Particle Swarm Optimization Combined with Ant Colony Optimization for the Multiple Traveling Salesman Problem." Materials Science Forum 626-627 (August 2009): 717–22. http://dx.doi.org/10.4028/www.scientific.net/msf.626-627.717.

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A lot of practical problem, such as the scheduling of jobs on multiple parallel production lines and the scheduling of multiple vehicles transporting goods in logistics, can be modeled as the multiple traveling salesman problem (MTSP). Due to the combinatorial complexity of the MTSP, it is necessary to use heuristics to solve the problem, and a discrete particle swarm optimization (DPSO) algorithm is employed in this paper. Particle swarm optimization (PSO) in the continuous space has obtained great success in resolving some minimization problems. But when applying PSO for the MTSP, a difficulty rises, which is to find a suitable mapping between sequence and continuous position of particles in particle swarm optimization. For overcoming this difficulty, PSO is combined with ant colony optimization (ACO), and the mapping between sequence and continuous position of particles is established. To verify the efficiency of the DPSO algorithm, it is used to solve the MTSP and its performance is compared with the ACO and some traditional DPSO algorithms. The computational results show that the proposed DPSO algorithm is efficient.

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20

Deroussi, Laurent, and David Lemoine. "Discrete Particle Swarm Optimization for the Multi-Level Lot-Sizing Problem." International Journal of Applied Metaheuristic Computing 2, no. 1 (January 2011): 44–57. http://dx.doi.org/10.4018/jamc.2011010104.

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This paper presents a Discrete Particle Swarm Optimization (DPSO) approach for the Multi-Level Lot-Sizing Problem (MLLP), which is an uncapacitated lot sizing problem dedicated to materials requirements planning (MRP) systems. The proposed DPSO approach is based on cost modification and uses PSO in its original form with continuous velocity equations. Each particle of the swarm is represented by a matrix of logistic costs. A sequential approach heuristic, using Wagner-Whitin algorithm, is used to determine the associated production planning. The authors demonstrate that any solution of the MLLP can be reached by particles. The sequential heuristic is a subjective function from the particles space to the set of the production plans, which meet the customer’s demand. The authors test the DPSO Scheme on benchmarks found in literature, more specifically the unique DPSO that has been developed to solve the MLLP.

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21

Li, Jiu Yong, and Jing Wang. "New Discrete Particle Swarm Algorithm for Traveling Salesman Problem." Advanced Materials Research 148-149 (October 2010): 210–14. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.210.

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In this paper, a novel algorithm called CIPSO for short based on particle optimization algorithm(PSO) and Chaos optimization Algorithm(COA) is presented to solve traveling salesman problem(TSP). We propose some new operators to solve the difficulties of implementing PSO into solving this discrete problem based on the special fitness landscape of TSP. Meanwhile embedded with chaos theory it can enhance particles’ global searching ability so as not to converge to the local optimal solution too quickly, and the introduction of information intercourse can enhance thire local searching ability. Comparing to SA, GA, ACS and so on, this new algorithm shows its validity and satisfactory effect on several benchmark test problems.

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22

Wu, Yi-Ling, Tsu-Feng Ho, Shyong Jian Shyu, and Bertrand M. T. Lin. "Discrete Particle Swarm Optimization with Scout Particles for Library Materials Acquisition." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/636484.

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Materials acquisition is one of the critical challenges faced by academic libraries. This paper presents an integer programming model of the studied problem by considering how to select materials in order to maximize the average preference and the budget execution rate under some practical restrictions including departmental budget, limitation of the number of materials in each category and each language. To tackle the constrained problem, we propose a discrete particle swarm optimization (DPSO) with scout particles, where each particle, represented as a binary matrix, corresponds to a candidate solution to the problem. An initialization algorithm and a penalty function are designed to cope with the constraints, and the scout particles are employed to enhance the exploration within the solution space. To demonstrate the effectiveness and efficiency of the proposed DPSO, a series of computational experiments are designed and conducted. The results are statistically analyzed, and it is evinced that the proposed DPSO is an effective approach for the studied problem.

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23

Lan, Xiu Ju, and Dan Dan Su. "Research of a Mold Job Shop Scheduling Optimization Based on Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 757 (April 2015): 201–7. http://dx.doi.org/10.4028/www.scientific.net/amm.757.201.

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Job shop scheduling is a key part of production management and control for manufacturing enterprises. An optimized scheduling is helpful for enterprise to strengthen its efficiency and competition. And particle swarm optimization is a young algorithm of swarm intelligence. So application and research of job shop scheduling based on particle swarm optimization has important practical significance. This paper analyze and diagnose the scheduling status of a mold manufacturing workshop, taking minimize make span and average of AI based on fuzzy processing-time and delivery as optimizing target, model the scheduling for the manufacturing of CQD-035. Eventually, programming on the platform of MATLAB7.0.1 using the discrete particle swarm algorithm, a satisfactory scheduling scheme is obtained.

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24

Huang, Dan Hua, and Su Wang. "An Improved Discrete Particle Swarm Optimization for Berth Scheduling Problem." Applied Mechanics and Materials 373-375 (August 2013): 1192–95. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1192.

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Berth scheduling operation is an important problem in container terminal. The mathematic model of this problem is described in this paper and an improved particle swarm optimization algorithm is introduced to obtain the optimal scheduling solution. A floating-point allocation rule is used to encode the particles in the discrete space. A local search method is combined with PSO to avoid precocity. Finally the experiments are done to prove the improved PSO in this paper can resolve the berth scheduling problem and get better solution and convergence speed than the basic PSO.

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Wang, Yong Sheng, Jun Li Li, and Yang Lou. "A Novel Centroid Particle Swarm Optimization Algorithm Based on Two Subpopulations." Applied Mechanics and Materials 29-32 (August 2010): 929–33. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.929.

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This paper proposed the concept of centroid in particle swarm optimization which is similar to physical centroid properties of objects. Similarly, we may think of a particle swarm as a discrete system of particles and find the centroid representing the entire population. Usually, it has a more promising position than worse particles among the population. In order to verify the role of centroid which can speed up the convergence rate of the algorithm, and prevent the algorithm from being trapped into a local solution early as far as possible at the same time, A Novel Centroid Particle Swarm Optimization Algorithm Based on Two Subpopulations(CPSO) is proposed. Numerical simulation experiments show that CPSO by testing some benchmark functions is better than Linear Decreasing Weight PSO (LDWPSO) in convergence speed in the same accuracy of solution case.

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26

Huang, Zhan Jun, An Na Wang, and Cui Lei. "Research and Analysis of Discrete PID Controller Parameters’ Optimization Based on Particle Swarm Optimization Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 1228–32. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1228.

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Currently, PID controllers have been widely used in industrial control. And computer control systems are widely used in the field environment. How to design a discrete PID controller which has a good control performance is very important for existing control objects. In this paper, particle swarm optimization and quadratic performance index are used to optimize the control parameters of the discrete PID controller. The optimized results are given at last. By simulation studies and analysis, the optimal parameters of the PID controller are found by particle swarm optimization algorithm, and the quadratic performance index has a good effect on optimal control.

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Wei, Na, Mingyong Liu, and Weibin Cheng. "Decision-Making of Underwater Cooperative Confrontation Based on MODPSO." Sensors 19, no. 9 (May 13, 2019): 2211. http://dx.doi.org/10.3390/s19092211.

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This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

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Noudjiep Djiepkop, Giresse Franck, and Senthil Krishnamurthy. "Multi-Objective Feeder Reconfiguration Using Discrete Particle Swarm Optimization." Mathematics 10, no. 3 (February 8, 2022): 531. http://dx.doi.org/10.3390/math10030531.

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Electric power distribution systems have been heavily engaged in evolutionary changes toward effective usage of distribution networks for dependability, quality, and improvement of services delivered to customers throughout the years. This was accomplished via a procedure known as reconfiguration. Several strategies have been offered by various authors for successful distribution feeder reconfiguration with a novel optimization method. As a result, this work developed a Discrete Particle Swarm Optimization (DPSO) method to address the issue of distribution system feeder reconfiguration during both steady-state and dynamic power system operations. In a dynamic state, the power demand and generation required are continually changing over time, and the DPSO algorithm finds a new set of solutions to fulfill the power demand. Many network topologies are investigated for the dynamic operation. The feeder reconfiguration single-objective optimization problem was transformed into a multi-objective optimization problem by taking into account both real power loss reduction and distribution system load balancing. The suggested technique was verified using various IEEE 16, 33, and 69 bus standard test distribution systems to determine the efficiency of the developed DPSO algorithm. The simulation findings reveal that DPSO outperforms other optimization algorithms in terms of actual power loss reduction and load balancing, while solving multi-objective distribution system feeder reconfiguration.

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29

Ma, Qing, Zhi Jun Long, Chang Hong Deng, Miao Li, Jia Bin You, and Yong Xiao. "Applications of Cloud Model Migration Particle Swarm Optimization and Gaussian Penalty Function in Reactive Power Optimization." Advanced Materials Research 986-987 (July 2014): 1365–69. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.1365.

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Анотація:

In order to cope with the defects of traditional particle swarm optimization (PSO) algorithm, such as its prematurity and deficiency in global optimization, a cloud model migration particle swarm optimization (CMMPSO) algorithm is proposed. Firstly, the X-condition generator based on Cloud model is introduced to adjust the inertia weights of particles; then migration action is implemented to lead the flight of global optimal particle. In allusion to the mixed integer programming problem of reactive power optimization, discrete variables are treated as continuous variables in early iterations, and a discretization operation based on Gaussian penalty function is conducted in later stages. Taking the minimum network loss and minimum voltage offset as objective functions, simulations of IEEE 30-bus system is performed to verify the feasibility and effectiveness of the proposed algorithm.

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30

MOORE, PHILLIP W., and GANESH K. VENAYAGAMOORTHY. "EVOLVING DIGITAL CIRCUITS USING HYBRID PARTICLE SWARM OPTIMIZATION AND DIFFERENTIAL EVOLUTION." International Journal of Neural Systems 16, no. 03 (June 2006): 163–77. http://dx.doi.org/10.1142/s0129065706000585.

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This paper presents the evolution of combinational logic circuits by a new hybrid algorithm known as the Differential Evolution Particle Swarm Optimization (DEPSO), formulated from the concepts of a modified particle swarm and differential evolution. The particle swarm in the hybrid algorithm is represented by a discrete 3-integer approach. A hybrid multi-objective fitness function is coined to achieve two goals for the evolution of circuits. The first goal is to evolve combinational logic circuits with 100% functionality, called the feasible circuits. The second goal is to minimize the number of logic gates needed to realize the feasible circuits. In addition, the paper presents modifications to enhance performance and robustness of particle swarm and evolutionary techniques for discrete optimization problems. Comparison of the performance of the hybrid algorithm to the conventional Karnaugh map and evolvable hardware techniques such as genetic algorithm, modified particle swarm, and differential evolution are presented on a number of case studies. Results show that feasible circuits are always achieved by the DEPSO algorithm unlike with other algorithms and the percentage of best solutions (minimal logic gates) is higher.

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31

Mühlenthaler, Moritz, and Alexander Raß. "Runtime analysis of discrete particle swarm optimization algorithms: A survey." it - Information Technology 61, no. 4 (August 27, 2019): 177–85. http://dx.doi.org/10.1515/itit-2019-0009.

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Abstract A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.

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32

Xing, Li Zhao, He Li Le, and Zhang Hui. "A Novel Social Network Structural Balance Based on the Particle Swarm Optimization Algorithm." Cybernetics and Information Technologies 15, no. 2 (June 1, 2015): 23–35. http://dx.doi.org/10.1515/cait-2015-0026.

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AbstractExploration of the structural balance of social networks is of great importance for theoretical analysis and practical use. This study modeled the structural balance of social networks as a mathematical optimization problem by using swarm intelligence, and an efficient discrete particle swarm optimization algorithm was proposed to solve the modeled optimization problem. To take advantage of the topologies of social networks in the algorithm design, the discrete representation of the particle was redefined, and the discrete particle update principles were redesigned. To validate the efficiency of the proposed algorithm, experiments were conducted using synthetic and real-world social networks. The experiments demonstrate that the proposed algorithm not only achieves a balanced social network structure, but also automatically detects the community topology of networks.

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33

Tseng, K.-Y., C.-B. Zhang, and C.-Y. Wu. "An Enhanced Binary Particle Swarm Optimization for Structural Topology Optimization." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 224, no. 10 (April 22, 2010): 2271–87. http://dx.doi.org/10.1243/09544062jmes2128.

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Particle swarm optimization (PSO), a heuristic optimization method, has been successfully applied in solving many optimization problems in real-value search space. The original binary particle swarm optimization (BPSO) uses the concept of bit flipping of the binary string to convert the velocity from a real code into a binary code. However, the conversion process cannot be reversed, and it is difficult to extend this framework to solve certain discrete optimization problems. An enhanced binary particle swarm algorithm is proposed in this study based on pure binary bit-string frameworks to deal with structural topology optimization problems. Further, two enhancement strategies, stress-based strategy and pair-switched strategy, were developed to improve the performance of the proposed algorithm for topology optimization of structure. The results of experimental cases demonstrated in this study show that the proposed enhanced binary particle swarm optimization (EBPSO) with two developed strategies is an efficient population-based approach for finding the optimal design for structural topology optimization problems of minimum compliance design and minimum weight design.

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34

Jia, Zhi-gang, and Xing-xuan Wang. "System Identification of Heat-Transfer Process of Frequency Induction Furnace for Melting Copper Based on Particle Swarm Algorithm." Journal of Control Science and Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/502828.

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An adaptive evolutionary strategy in standard particle swarm optimization is introduced. Adaptive evolution particle swarm optimization is constructed to improve the capacity of global search. A method based on adaptive evolution particle swarm optimization for identification of continuous system with time delay is proposed. The basic idea is that the identification of continuous system with time delay is converted to an optimization of continuous nonlinear function. The adaptive evolution particle swarm optimization is utilized to find an optimal solution of continuous nonlinear function. Convergence conditions are given by the convergence analysis based on discrete time linear dynamic system theory. Numerical simulation results show that the proposed method is effective for a general continuous system with time delay and the system of heat-transfer process of frequency induction furnace for melting copper.

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35

Wang, Ji Hong, Wen Xiao Shi, Ke Qiang Cui, and Feng Jin. "Partially Overlapped Channel Assignment Using Discrete Particle Swarm Optimization." Applied Mechanics and Materials 614 (September 2014): 550–53. http://dx.doi.org/10.4028/www.scientific.net/amm.614.550.

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Wireless mesh networks are multi-hop wireless networks whose capacity decreases very fast due to interference from parallel transmissions on the same channel when using orthogonal channels. In order to further eliminate network interference and improve network capacity, partially overlapped channels are used to perform channel assignment, and a discrete particle swarm optimization (DPSO) based channel assignment algorithm using partially overlapped channels is proposed in this paper. Channel assignment for all links is mapped to position of particle, and DPSO is used to evolve and produce better channel assignment solutions. Simulation results show that network performances can be dramatically improved by properly utilizing partially overlapped channels, for example, the average end-to-end delay and the average packet loss ratio both can be decreased by at least 15 percent, and the network throughput can be improved by 12 percent or more.

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36

Senthilnath, J., S. N. Omkar, V. Mani, and T. Karthikeyan. "Multiobjective Discrete Particle Swarm Optimization for Multisensor Image Alignment." IEEE Geoscience and Remote Sensing Letters 10, no. 5 (September 2013): 1095–99. http://dx.doi.org/10.1109/lgrs.2012.2230432.

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37

Wu, Huayao, Changhai Nie, Fei-Ching Kuo, Hareton Leung, and Charles J. Colbourn. "A Discrete Particle Swarm Optimization for Covering Array Generation." IEEE Transactions on Evolutionary Computation 19, no. 4 (August 2015): 575–91. http://dx.doi.org/10.1109/tevc.2014.2362532.

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38

Ezzeldin, Riham, Berge Djebedjian, and Tarek Saafan. "Integer Discrete Particle Swarm Optimization of Water Distribution Networks." Journal of Pipeline Systems Engineering and Practice 5, no. 1 (February 2014): 04013013. http://dx.doi.org/10.1061/(asce)ps.1949-1204.0000154.

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39

Gong, Maoguo, Yue Wu, Qing Cai, Wenping Ma, A. K. Qin, Zhenkun Wang, and Licheng Jiao. "Discrete particle swarm optimization for high-order graph matching." Information Sciences 328 (January 2016): 158–71. http://dx.doi.org/10.1016/j.ins.2015.08.038.

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40

Muthuswamy, Shanthi, and Sarah S. Lam. "Discrete particle swarm optimization for the team orienteering problem." Memetic Computing 3, no. 4 (October 15, 2011): 287–303. http://dx.doi.org/10.1007/s12293-011-0071-x.

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41

García-Villoria, Alberto, and Rafael Pastor. "Introducing dynamic diversity into a discrete particle swarm optimization." Computers & Operations Research 36, no. 3 (March 2009): 951–66. http://dx.doi.org/10.1016/j.cor.2007.12.001.

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42

Yu, Haizhen, Pengjun Wang, Disheng Wang, and Huihong Zhang. "Discrete ternary particle swarm optimization for area optimization of MPRM circuits." Journal of Semiconductors 34, no. 2 (February 2013): 025011. http://dx.doi.org/10.1088/1674-4926/34/2/025011.

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43

Lian, Zhigang. "A Local and Global Search Combine Particle Swarm Optimization Algorithm for Job-Shop Scheduling to Minimize Makespan." Discrete Dynamics in Nature and Society 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/838596.

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The Job-shop scheduling problem (JSSP) is a branch of production scheduling, which is among the hardest combinatorial optimization problems. Many different approaches have been applied to optimize JSSP, but for some JSSP even with moderate size cannot be solved to guarantee optimality. The original particle swarm optimization algorithm (OPSOA), generally, is used to solve continuous problems, and rarely to optimize discrete problems such as JSSP. In OPSOA, through research I find that it has a tendency to get stuck in a near optimal solution especially for middle and large size problems. The local and global search combine particle swarm optimization algorithm (LGSCPSOA) is used to solve JSSP, where particle-updating mechanism benefits from the searching experience of one particle itself, the best of all particles in the swarm, and the best of particles in neighborhood population. The new coding method is used in LGSCPSOA to optimize JSSP, and it gets all sequences are feasible solutions. Three representative instances are made computational experiment, and simulation shows that the LGSCPSOA is efficacious for JSSP to minimize makespan.

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44

Dong, Xu Chu, Dan Tong Ouyang, Dian Bo Cai, Yu Xin Ye, and Sha Sha Feng. "A Robust Cooperative Coevolutionary Particle Swarm Optimization Algorithm for Triangulation of Bayesian Networks." Advanced Materials Research 181-182 (January 2011): 468–73. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.468.

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In this paper, a cooperative coevoluationary particle swarm optimization algorithm, CCMDPSO, is proposed to solve the optimization problem of triangulation of Bayesian networks. It arranges all the variables of a given Bayesian network into some groups according to the global best solution and performs optimization on these small-scale groups. The basic optimizer of CCMDPSO is an improved discrete particle swarm optimization algorithm, MDPSO. Experiments show that CCMDPSO is an effective and robust method for the triangulation problem.

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45

Goudos, Sotirios K., Zaharias D. Zaharis, and Konstantinos B. Baltzis. "Particle Swarm Optimization as Applied to Electromagnetic Design Problems." International Journal of Swarm Intelligence Research 9, no. 2 (April 2018): 47–82. http://dx.doi.org/10.4018/ijsir.2018040104.

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Particle swarm optimization (PSO) is a swarm intelligence algorithm inspired by the social behavior of birds flocking and fish schooling. Numerous PSO variants have been proposed in the literature for addressing different problem types. In this article, the authors apply different PSO variants to common design problems in electromagnetics. They apply the Inertia Weight PSO (IWPSO), the Constriction Factor PSO (CFPSO), and the Comprehensive Learning Particle Swarm Optimization (CLPSO) algorithms to real-valued optimization problems, i.e. microwave absorber design, and linear array synthesis. Moreover, the authors use discrete PSO optimizers such as the binary PSO (binPSO) and the Boolean PSO with a velocity mutation (BPSO-vm) in order to solve discrete-valued optimization problems, i.e. patch antenna design. Additionally, the authors apply and compare binPSO with different transfer functions to thinning array design problems. In the case of a multi-objective optimization problem, they apply two multi-objective PSO variants to dual-band base station antenna optimization for mobile communications. Namely, these are the Multi-Objective PSO (MOPSO) and the Multi-Objective PSO with Fitness Sharing (MOPSO-fs) algorithms. Finally, the authors conclude the paper by providing a discussion on future trends and the conclusion.

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46

Wang, Miao, Yuhua Huang, and Jindong Zhang. "Current Situation and Development of Advanced Planning and Scheduling System Based on Group Optimization Algorithm in Discrete Industry." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 215–20. http://dx.doi.org/10.54097/hset.v23i.3270.

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Discrete industry, especially job shop scheduling, has always been the key industry of Advanced Planning and Scheduling system (APS) system application. Based on Particle Swarm optimization (PSO), this paper introduces the Group swarm optimization algorithm, expounds the relevant theory and development status of APS, introduces the application of Particle Swarm optimization and Artificial Bee Colony optimization algorithm in APS system, and analyzes the performance and efficiency of the two algorithms. Finally, it predicts the future development trend of APS: the core algorithm will adopt a variety of hybrid algorithms, and the data flow will be combined with ERP / MES system.

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47

Elilraja, D., and Sundaravel Vijayan. "Particle Swarm Optimization for Integrated Fixture Layout." Applied Mechanics and Materials 787 (August 2015): 285–90. http://dx.doi.org/10.4028/www.scientific.net/amm.787.285.

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Fixture is a work-holding or supporting device used in the manufacturing industry to hold the workpiece. Fixtures are used to securely locate (position in a specific location or orientation) and support the work, ensuring that all parts produced using the fixture will maintain conformity and interchangeability. The location of fixture elements is called as fixture layout. The fixture layout plays major role in the work piece deformation during the machining operation. Hence optimization of fixture layout to minimize the work piece deformation is one of the critical aspects in the fixture design process. Minimization the workpiece deformation which is the objective function in the present work is calculated using Finite Element Method (FEM) and the fixture layout is optimized using Discrete fixture layout optimization method (DFLOM), Continuous fixture layout optimization method (CFLOM) and Integrated fixture layout optimization method (IFLOM).The workpiece deformation is minimum in Particle Swarm Optimization (PSO) based IFLOM is reported for the selected fixture. In this paper the PSO is used as an optimization tool to optimize the workpiece deformation.

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48

Guo, Sha-sha, Jie-sheng Wang, and Meng-wei Guo. "Z-Shaped Transfer Functions for Binary Particle Swarm Optimization Algorithm." Computational Intelligence and Neuroscience 2020 (June 8, 2020): 1–21. http://dx.doi.org/10.1155/2020/6502807.

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Particle swarm optimization (PSO) algorithm is a swarm intelligent searching algorithm based on population that simulates the social behavior of birds, bees, or fish groups. The discrete binary particle swarm optimization (BPSO) algorithm maps the continuous search space to a binary space through a new transfer function, and the update process is designed to switch the position of the particles between 0 and 1 in the binary search space. Aiming at the existed BPSO algorithms which are easy to fall into the local optimum, a new Z-shaped probability transfer function is proposed to map the continuous search space to a binary space. By adopting nine typical benchmark functions, the proposed Z-probability transfer function and the V-shaped and S-shaped transfer functions are used to carry out the performance simulation experiments. The results show that the proposed Z-shaped probability transfer function improves the convergence speed and optimization accuracy of the BPSO algorithm.

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49

Bartoccini, Umberto, Arturo Carpi, Valentina Poggioni, and Valentino Santucci. "Memes Evolution in a Memetic Variant of Particle Swarm Optimization." Mathematics 7, no. 5 (May 11, 2019): 423. http://dx.doi.org/10.3390/math7050423.

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In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.

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50

Guner, Ali R., and Mehmet Sevkli. "A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem." Journal of Artificial Evolution and Applications 2008 (April 8, 2008): 1–9. http://dx.doi.org/10.1155/2008/861512.

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A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

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