Статті в журналах: "Bee swarm algorithm"

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Sharma, Harish, Jagdish Chand Bansal, K. V. Arya, and Kusum Deep. "Dynamic Swarm Artificial Bee Colony Algorithm." International Journal of Applied Evolutionary Computation 3, no. 4 (October 2012): 19–33. http://dx.doi.org/10.4018/jaec.2012100102.

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Artificial Bee Colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over test problems as well as real world optimization problems. This paper presents an attempt to modify ABC to make it less susceptible to stick at local optima and computationally efficient. In the case of local convergence, addition of some external potential solutions may help the swarm to get out of the local valley and if the algorithm is taking too much time to converge then deletion of some swarm members may help to speed up the convergence. Therefore, in this paper a dynamic swarm size strategy in ABC is proposed. The proposed strategy is named as Dynamic Swarm Artificial Bee Colony algorithm (DSABC). To show the performance of DSABC, it is tested over 16 global optimization problems of different complexities and a popular real world optimization problem namely Lennard-Jones potential energy minimization problem. The simulation results show that the proposed strategies outperformed than the basic ABC and three recent variants of ABC, namely, the Gbest-Guided ABC, Best-So-Far ABC and Modified ABC.

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Yong, Wang, Wang Tao, Zhang Cheng-Zhi, and Huang Hua-Juan. "A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm." International Journal of Computational Intelligence and Applications 15, no. 02 (June 2016): 1650011. http://dx.doi.org/10.1142/s1469026816500115.

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A novel nature-inspired swarm intelligence (SI) optimization is proposed called dolphin swarm optimization algorithm (DSOA), which is based on mimicking the mechanism of dolphins in detecting, chasing after, and preying on swarms of sardines to perform optimization. In order to test the performance, the DSOA is evaluated against the corresponding results of three existing well-known SI optimization algorithms, namely, particle swarm optimization (PSO), bat algorithm (BA), and artificial bee colony (ABC), in the terms of the ability to find the global optimum of a range of the popular benchmark functions. The experimental results show that the proposed optimization seems superior to the other three algorithms, and the proposed algorithm has the performance of fast convergence rate, and high local optimal avoidance.

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Verma, Balwant Kumar, and Dharmender Kumar. "A review on Artificial Bee Colony algorithm." International Journal of Engineering & Technology 2, no. 3 (June 21, 2013): 175. http://dx.doi.org/10.14419/ijet.v2i3.1030.

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In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity, easy to implementation and fewer control parameters. Large numbers of problems have been solved using ABC algorithm such as travelling salesman problem, clustering, routing, scheduling etc. the aim of this paper is to provide up to date enlightenment in the field of ABC algorithm and its applications.

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Balasubramani, Kamalam, and Karnan Marcus. "A Comprehensive review of Artificial Bee Colony Algorithm." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 5, no. 1 (June 23, 2013): 15–28. http://dx.doi.org/10.24297/ijct.v5i1.4382.

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The Artificial Bee Colony (ABC) algorithm is a stochastic, population-based evolutionary method proposed by Karaboga in the year 2005. ABC algorithm is simple and very flexible when compared to other swarm based algorithms. This method has become very popular and is widely used, because of its good convergence properties. The intelligent foraging behavior of honeybee swarm has been reproduced in ABC.Numerous ABC algorithms were developed based on foraging behavior of honey bees for solving optimization, unconstrained and constrained problems. This paper attempts to provide a comprehensive survey of research on ABC. A system of comparisons and descriptions is used to designate the importance of ABC algorithm, its enhancement, hybrid approaches and applications.

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Dahiya, Brahm Prakash, Shaveta Rani, and Paramjeet Singh. "A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space." International Journal of Mathematical, Engineering and Management Sciences 4, no. 2 (April 1, 2019): 471–88. http://dx.doi.org/10.33889/ijmems.2019.4.2-039.

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Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimization (HAGOA) meta-heuristic algorithm is proposed to improve the exploitation and exploration in given search space. HAGOA is inherited Salp swarm behaviors. HAGOA performs balancing in exploitation and exploration search space. It is capable to make chain system between exploitation and exploration phases. The efficiency of HAGOA meta-heuristic algorithm will analyze using 19 benchmarks functions from F1 to F19. In this paper, HAGOA algorithm is performed efficiency analyze test with Artificial Grasshopper optimization (AGOA), Hybrid Artificial Bee Colony with Salp (HABCS), Modified Artificial Bee Colony (MABC), and Modify Particle Swarm Optimization (MPSO) swarm based meta-heuristic algorithms using uni-modal and multi-modal functions in MATLAB. Comparison results are shown that HAGOA meta-heuristic algorithm is performed better efficiency than other swarm intelligence algorithms on the basics of high exploitation, high exploration, and high convergence rate. It also performed perfect balancing between exploitation and exploration in given search space.

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Devarajan, Jinil Persis, and T. Paul Robert. "Swarm Intelligent Data Aggregation in Wireless Sensor Network." International Journal of Swarm Intelligence Research 11, no. 2 (April 2020): 1–18. http://dx.doi.org/10.4018/ijsir.2020040101.

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Data aggregation in WSNs is an interesting problem wherein data sensed by the sensors is routed to an aggregation node in an efficient way. Since the sensors are battery operated, it is very important for a routing protocol to conserve energy and also ensure load balancing and faster delivery. In this study, a multi-objective linear programming model is developed for this problem and solved using an exact algorithm applying dominance principle. In order to ensure faster convergence, routing algorithms incorporating strategies of swarms in nature such as Ants, Bees and Fireflies are adapted. In the simulation study, it is quite evident from the convergence characteristics, swarm intelligent algorithms could converge earlier than the exact algorithm with convergence time lesser by 90%. Moreover, when exact algorithm could solve smaller networks, the swarm intelligent algorithms could solve even larger network instances. Firefly algorithm is able to yield approximated pareto – optimal routes which outperforms ant colony optimization and bee colony optimization algorithms.

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Sobecki, Janusz. "Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application." International Journal of Software Engineering and Knowledge Engineering 24, no. 01 (February 2014): 91–109. http://dx.doi.org/10.1142/s0218194014500041.

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In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).

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8

Chun-Feng, Wang, Liu Kui, and Shen Pei-Ping. "Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/832949.

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Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

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9

Zou, Wenping, Yunlong Zhu, Hanning Chen, and Xin Sui. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm." Discrete Dynamics in Nature and Society 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/459796.

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Artificial Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO), and its cooperative version (CPSO) are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

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10

Xu, Xiao Qiang, and De Ming Lei. "Research on Swarm Intelligence Algorithm with an Artificial Bee Colony Algorithm for Lot Streaming Problem in Job Shop." Advanced Materials Research 951 (May 2014): 239–44. http://dx.doi.org/10.4028/www.scientific.net/amr.951.239.

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The lot streaming (LS) problem in job shop with equal-size sub-lots and intermittent idling is considered. An effective swarm intelligence algorithm with an artificial bee colony (ABC) algorithm is proposed for the minimization of total penalties of tardiness and earliness. In the first period of ABC, the employed bee phase and the onlooker bee phase are both for lot/sub-lot scheduling. In the second period, the LS conditions are determined in the employed bee phase and the lot/sub-lot is scheduled in the onlooker phase. The worst solution of the swarm is replaced with the elite one every few cycles. Computational results show the promising advantage of ABC.

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Luo, You Xin, and Xiao Yi Che. "Chaotic Artificial Bee Colony Constrained Optimization Algorithm with Hybrid Discrete Variables and its Application to Mechanical Optimization." Applied Mechanics and Materials 155-156 (February 2012): 211–15. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.211.

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Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences into the algorithm. On the basis of Artificial Bee Colony Optimization Algorithm, a new algorithm by introducing constructing dynamic penalty function and chaotic sequences was presented. Based on Matlab software, the program CABCOA1.0 with hybrid discrete variables for the proposed algorithm was developed. The complex mechanical optimization was given. The results show that this algorithm has no special requirements on the characteristics of optimal designing problems, which has a fairly good universal adaptability and a reliable operation of program with a strong ability of overall convergence.

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12

Bindu M. G. and Sabu M. K. "GABC." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 78–95. http://dx.doi.org/10.4018/ijsir.2021070104.

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Feature selection is a complex pre-processing step in data mining that enhances classification accuracy by selecting the minimum number of relevant features. Artificial bee colony algorithm (ABC) is one of the successful swarm intelligent algorithms for feature selection, image processing, data analytics, protein structure prediction, etc. It simulates the honey foraging behavior of the bee swarm. But it tends to low convergence speed and local optima stagnation. Hybrid meta-heuristics can enhance the performance of existing swarm algorithms. This paper proposes a hybrid approach for the ABC algorithm by incorporating genetic operators into it. The mutation operator is used to explore the better-quality neighborhood while the crossover is used to enhance the quality of solutions by implementing diversity into them. The performance of the proposed method is evaluated using UCI data sets and compared with existing swarm algorithms for feature selection. The effectiveness of the proposed method is evident from the results.

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Kulkarni, Vaishali Raghavendra, and Veena Desai. "Sensor Localization in Wireless Sensor Networks Using Cultural Algorithm." International Journal of Swarm Intelligence Research 11, no. 4 (October 2020): 106–22. http://dx.doi.org/10.4018/ijsir.2020100105.

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Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.

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Alqattan, Zakaria N., and Rosni Abdullah. "A hybrid artificial bee colony algorithm for numerical function optimization." International Journal of Modern Physics C 26, no. 10 (June 24, 2015): 1550109. http://dx.doi.org/10.1142/s0129183115501090.

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Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

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Ewald, Dawid, Hubert Zarzycki, Łukasz Apiecionek, and Jacek Czerniak. "Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems." JUCS - Journal of Universal Computer Science 26, no. 11 (November 28, 2020): 1475–94. http://dx.doi.org/10.3897/jucs.2020.078.

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The paper presents an innovative OFNBee optimization method based on combining the swarm intelligence with the use of directed fuzzy numers OFN. In the introduction, the issues related to the subject of the study, including bee algorithms and OFN numbers, were reviewed. The innovative OFNBee algorithm was presented and verified against a set of known benchmarks functions such as Sphere, Rastrigin, Griewank, Rosenbrock, Schwefel and Ackley. These functions have been applied due to their reliability in the literature. In the further part of the study, the configuration of the algorithm parameters is carried out, including the launch of each mathematical function several dozen times for different data, such as different population sizes. The key part of the research and analysis was to compare OFNBee with six standard ABC, MBO, IMBO, TLBO, HBMO, BBMO bee algorithms. The article ends with a summary and an indication of the possible future works.

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Patel, Subhash, and Rajesh A. Thakker. "Parameter Space Exploration for Analog Circuit Design Using Enhanced Bee Colony Algorithm." Journal of Circuits, Systems and Computers 28, no. 09 (August 2019): 1950153. http://dx.doi.org/10.1142/s0218126619501536.

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In this work, novel swarm optimization algorithm based on the Artificial Bee Colony (ABC) algorithm called Enhanced Artificial Bee Colony (EABC) algorithm is proposed for the design and optimization of the analog CMOS circuits. The new search strategies adopted improve overall performance of the proposed algorithm. The performance of EABC algorithm is compared with other competitive algorithms such as ABC, GABC (G-best Artificial Bee Colony Algorithm) and MABC (Modified Artificial Bee Colony Algorithm) by designing three CMOS circuits; Two-stage operational amplifier, low-voltage bulk driven OTA and second generation low-voltage current conveyor in 0.13 [Formula: see text]m and 0.09[Formula: see text][Formula: see text]m CMOS technologies. The obtained results clearly indicate that the performance of EABC algorithm is better than other mentioned algorithms and it can be an effective approach for the automatic design of the analog CMOS circuits.

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Kareem, Shahab Wahhab, Shavan Askar, Roojwan Sc Hawezi, Glena Aziz Qadir, and Dina Yousif Mikhail. "A comparative Evaluation of Swarm Intelligence Algorithm Optimization: A Review." Journal of Electronics, Electromedical Engineering, and Medical Informatics 3, no. 3 (October 4, 2021): 111–18. http://dx.doi.org/10.35882/jeeemi.v3i3.1.

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Swarm intelligence (SI), an important aspect of artificial intelligence, is increasingly gaining popularity as more and more high-complexity challenges necessitate solutions that are sub-optimal but still feasible in a fair amount of time. Artificial intelligence that mimics the collective behavior of a group of animals is known as swarm intelligence. Attempting to survive. It is primarily influenced by biological systems. The main aim of our article is to find out more about the guiding principle, classify possible implementation areas, and include a thorough analysis of several SI algorithms. Swarms can be observed in ant colonies, fish schools, bird flocks, among other fields. During this article, we will look at some Swarm instances and their behavior. We see many Swarm Intelligence systems, like Ant colony Optimization, which explains ant activity, nature, and how they conquer challenges; in birds, we see Particle Swarm Optimization is a swarm intelligence-based optimization technique, and how the locations must be positioned based on the three concepts. The Bee Colony Optimization follows, and explores the behavior of bees, their relationships, as well as movement and how they work in a swarm. This paper explores some of the methods and algorithms.

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Düğenci, Muharrem, and Mehmet Emin Aydin. "A honeybees-inspired heuristic algorithm for numerical optimisation." Neural Computing and Applications 32, no. 16 (October 16, 2019): 12311–25. http://dx.doi.org/10.1007/s00521-019-04533-x.

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Abstract Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.

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Misevičius, Alfonsas, Jonas Blonskis, and Vytautas Bukšnaitis. "Bičių spiečių imitavimas sprendžiant optimizavimo uždavinius." Informacijos mokslai 56 (January 1, 2011): 163–73. http://dx.doi.org/10.15388/im.2011.0.3140.

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Straipsnyje nagrinėjami klausimai, susiję su naujoviškų metodų taikymu sprendžiant optimizavimo uždavinius. Šiuo konkrečiu atveju diskutuojama apie bičių spiečių elgsenos imitavimą ir galimą jo taikymą kombinatorinio (diskretinio) tipo optimizavimo uždaviniams. Straipsnio pradžioje aptariami konceptualūs aspektai ir bendroji bičių spiečių imitavimo algoritmų idėja. Aprašoma bičių spiečiaus imitavimo algoritmo realizacija atskiram nagrinėjamam atvejui – kvadratinio paskirstymo uždaviniui, kuris yra vienas iš aktualių ir sudėtingų kombinatorinio optimizavimo uždavinių pavyzdžių. Straipsnyje pateikiami ir su realizuotu algoritmu atliktų eksperimentų rezultatai, kurie iliustruoja skirtingų veiksnių (parametrų) įtaką gaunamų sprendinių kokybei ir patvirtina aukštą algoritmo efektyvumo lygį.Bee Swarm Intelligence in (Combinatorial) OptimizationAlfonsas Misevičius, Jonas Blonskis, Vytautas Bukšnaitis SummaryIn this paper, we discuss some issues related to the innovative intelligent optimization methods. More precisely, we are concerned with the bee colony optimization approach, which is inspired by the behaviour of natural swarms of honey bees. Both the conceptual methodological facets of the swarm intelligence paradigm and the aspects of implementation of the artificial bee colony algorithms are considered. In particular, we introduce an implementation of the artificial bee colony optimization algorithm for the well-known combinatorial optimization problem of quadratic assignment (QAP). The results of computational experiments with different variants of the implemented algorithm are also presented and discussed. Based on the obtained results, it is concluded that the proposed algorithm may compete with other efficient heuristic techniques.

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Dou, Siqi, Junjie Li, and Fei Kang. "Parameter identification of concrete dams using swarm intelligence algorithm." Engineering Computations 34, no. 7 (October 2, 2017): 2358–78. http://dx.doi.org/10.1108/ec-03-2017-0110.

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Purpose Parameter identification is an important issue in structural health monitoring and damage identification for concrete dams. The purpose of this paper is to introduce a novel adaptive fireworks algorithm (AFWA) into inverse analysis of parameter identification. Design/methodology/approach Swarm intelligence algorithms and finite element analysis are integrated to identify parameters of hydraulic structures. Three swarm intelligence algorithms: AFWA, standard particle swarm optimization (SPSO) and artificial bee colony algorithm (ABC) are adopted to make a comparative study. These algorithms are introduced briefly and then tested by four standard benchmark functions. Inverse analysis methods based on AFWA, SPSO and ABC are adopted to identify Young’s modulus of a concrete gravity dam and a concrete arch dam. Findings Numerical results show that swarm intelligence algorithms are powerful tools for parameter identification of concrete structures. The proposed AFWA-based inverse analysis algorithm for concrete dams is promising in terms of accuracy and efficiency. Originality/value Fireworks algorithm is applied for inverse analysis of hydraulic structures for the first time, and the problem of parameter selection in AFWA is studied.

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Reddy T, Chandrasekhara, Srivani V, A. Mallikarjuna Reddy, and G. Vishnu Murthy. "Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm." International Journal of Engineering & Technology 7, no. 4.6 (September 25, 2018): 275. http://dx.doi.org/10.14419/ijet.v7i4.6.20489.

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For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our proposed technique test cases are optimized by utilizing Improved Cuckoo Algorithm (ICSA). At that point, the advanced experiments are organized or prioritized by utilizing Particle Swarm Optimization algorithm (PSO). The Particle Swarm Optimization and Improved Cuckoo Algorithm (PSOICSA) estimation is a blend of Improved Cuckoo Search Algorithm(ICSA) and Particle Swarm Optimization (PSO). PSOICSA could be utilized to advance the test suite, and coordinate both ICSA and PSO for a superior outcome, when contrasted with their individual execution as far as experiment improvement.

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Abunaser, Amal Mahmoud, and Sawsan Alshattnawi. "Hybridizing Artificial Bee Colony Algorithm with Multi-Parent Crossover Operator." International Journal of Applied Metaheuristic Computing 6, no. 2 (April 2015): 18–32. http://dx.doi.org/10.4018/ijamc.2015040102.

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Artificial Bee Colony algorithm (ABC) is a new optimization algorithms used to solve several optimization problems. The algorithm is a swarm-based that simulates the intelligent behavior of honey bee swarm in searching for food sources. Several variations of ABC have been three existing solution vectors, the new solution vectors will replace the worst three vectors in the food source proposed to enhance its performance. This paper proposes a new variation of ABC that uses multi-parent crossover named multi parent crossover operator artificial bee colony (MPCO-ABC). In the proposed technique the crossover operator is used to generate three new parents based on memory (FSM). The proposed algorithm has been tested using a set of benchmark functions. The experimental results of the MPCO-ABC are compared with the original ABC, GABC. The results prove the efficiency of MPCO-ABC over ABC. Another comparison of MPCO-ABC results made with the other variants of ABC that use crossover and/or mutation operator. The MPCO-ABC almost always shows superiority on all test functions.

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Kaushal, Payal, Meenu Khurana, and K. R. Ramkumar. "A Systematic Review of Swarm Intelligence Algorithms to Perform Routing for VANETs Communication." ECS Transactions 107, no. 1 (April 24, 2022): 5027–35. http://dx.doi.org/10.1149/10701.5027ecst.

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The performance of Vehicular Adhoc Networks (VANETs), the underlying technology for intelligent vehicles, has shown improvement with the application of swarm-based algorithms for routing. Swarm Intelligence is a self-intelligence group of similar agents functioning on distribution, flexibility, and communication among the agents. The multiple problems occurring in the modern communication systems, including VANETs, have been tried to be resolved by the application of swarm intelligence algorithms viz, Genetic Algorithms (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuck-oo Search Algorithm (CSA), Glowworm Swarm Optimization (GSO), etc., have been proposed in the literature. This paper provides a comparative analysis of operations of swarm-based algorithms. Points like their operations, basic entities, advantages, disadvantages, and applications are discussed.

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Hasan, Luma Salal. "Artificial Bee Colony Algorithm and Bat Algorithm for Solving Travel Salesman Problem." Webology 19, no. 1 (January 20, 2022): 4185–93. http://dx.doi.org/10.14704/web/v19i1/web19276.

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There are many algorithms for optimization meta heuristic have developed on swarm intelligence-based that depending in the nature of design. One of them, Bat Algorithm (BA), is based on the "echolocation behaviors" of bats. Micro bat used echolocation to specify the prey, avoid obstacles and locate their roosting crevices in the dark. Another algorithm is Artificial Bee Colony (ABC) is a new optimization algorithm which depend on the "bee behavior" towards colony to search about the food. In this paper, presents the BA and ABC algorithms steps for solving TSP then try to search about best solution depending on the parameters for both algorithms. The results show the ABC is best performance than BA for finding the best tour quickly compared with other by consuming time lesser than by effecting on the convergence speed for searching the solution. Furthermore, The BA required more parameters to achieve each output efficiently and need using improved control strategy to balance between exploitation and exploration that consuming more time for it.

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Talatahari, S., H. Mohaggeg, Kh Najafi, and A. Manafzadeh. "Solving Parameter Identification of Nonlinear Problems by Artificial Bee Colony Algorithm." Mathematical Problems in Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/479197.

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A new optimization method based on artificial bee colony (ABC) algorithm is presented for solving parameter identification problems. The ABC algorithm as a swarm intelligent optimization algorithm is inspired by honey bee foraging. In this paper, for the first time, the ABC method is developed to determine the optimum parameters of Bouc-Wen hysteretic systems. The proposed method exhibits efficiency, robustness, and insensitivity to noise-corrupted data. The results of the ABC are compared with those other optimization algorithms from the literature to show the efficiency of this technique for solving parameter identification problems.

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Sun, Lijun, Tianfei Chen, and Qiuwen Zhang. "An Artificial Bee Colony Algorithm with Random Location Updating." Scientific Programming 2018 (August 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/2767546.

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As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension.

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Yuan, Zhen, Ya Zhou, Wei Lan Zhong, and Li Zhou. "Extensive Particle Swarm Artificial Bee Colony Algorithm for Function Optimization." Applied Mechanics and Materials 496-500 (January 2014): 1808–11. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1808.

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An extensive particle swarm artificial bee colony algorithm is proposed, which integrates the global best solution into the solution search equation of artificial bee colony to improve the exploitation. The memory weight and neighborhood dynamic step are introduced to keep the balance between the global search and local search, and to improve the search accuracy. Particle swarm optimization is embedded into the modified algorithm for on-line parameter optimizing. The simulations have shown that the new algorithm outperforms the ABC algorithm on search accuracy, convergence rate and global search capability. It has been found many applications in optimization of manufacturing and design process.

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Agrawal, Prateek, Harjeet Kaur, and Deepa Bhardwaj. "ANALYSIS AND SYNTHESIS OF ENHANCED BEE COLONY OPTIMIZATION WITH THE TRADITIONAL BEE COLONY OPTIMIZATION TO SOLVE TRAVELLING SALES PERSON PROBLEM." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 2 (April 30, 2012): 93–97. http://dx.doi.org/10.24297/ijct.v2i2b.6734.

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Bee colony optimization is the recent swarm intelligence technique which has been applied to solve many combinatorial problems. In this paper we propose the enhanced bee algorithm based on Kmeans clustering to solve TSP. In the proposed algorithm parallel bee algorithm has been applied to each cluster and connection method has been suggested to combine the sub tour to global tour of the whole cities. It was found the enhanced bee algorithm give the better result and more optimal tour.

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Acherjee, Bappa, Debanjan Maity, and Arunanshu S. Kuar. "Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms." International Journal of Applied Metaheuristic Computing 11, no. 2 (April 2020): 1–26. http://dx.doi.org/10.4018/ijamc.2020040101.

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The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravitational search algorithms (GSAs), genetic algorithms (GAs), particle swarm optimization (PSO) algorithms, ant colony optimization (ACO) algorithms and artificial bee colony (ABC) algorithms, and gives exactly the same results as predicted by the fireworks algorithm (FWA). The CS algorithm outperforms all other algorithms namely CSO, FWA, GSA, GA, PSO, ACO, and ABC algorithms in terms of mean computational time, whereas, the CSO algorithm outperforms all other algorithms except for the CS and GSA algorithms.

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Gorji-Bandpy, M., and A. Mozaffari. "Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/652391.

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A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). According to obtained results, it can be concluded that Mutable Smart Bee (MSB) is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

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Witkowski, Tadeusz. "Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems." Technical Sciences 1, no. 22 (February 7, 2019): 61–74. http://dx.doi.org/10.31648/ts.4348.

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This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real production scheduling problem too. The experiment results indicate that this problem can be effectively solved by PSO and DABC algorithms.

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32

Wu, Hu-Sheng, and Feng-Ming Zhang. "Wolf Pack Algorithm for Unconstrained Global Optimization." Mathematical Problems in Engineering 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/465082.

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The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA). Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.

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Gireesha. B, Mr, and . "A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 455. http://dx.doi.org/10.14419/ijet.v7i4.5.20205.

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From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle Swarm Optimization (PSO) is presented and also the most suitable fitness functions and its numerical expressions have discussed.

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Matrenin, P., V. Myasnichenko, N. Sdobnyakov, D. Sokolov, S. Fidanova, L. Kirilov, and R. Mikhov. "Generalized swarm intelligence algorithms with domain-specific heuristics." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 1 (March 1, 2021): 157. http://dx.doi.org/10.11591/ijai.v10.i1.pp157-165.

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<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

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35

Niknam, Taher, and Faranak Golestaneh. "Enhanced Bee Swarm Optimization Algorithm for Dynamic Economic Dispatch." IEEE Systems Journal 7, no. 4 (December 2013): 754–62. http://dx.doi.org/10.1109/jsyst.2012.2191831.

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36

Huang, Xingwang, Xuewen Zeng, Rui Han, and Xu Wang. "An enhanced hybridized artificial bee colony algorithm for optimization problems." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 87. http://dx.doi.org/10.11591/ijai.v8.i1.pp87-94.

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Artificial bee colony (ABC) algorithm is a popular swarm intelligence based algorithm. Although it has been proven to be competitive to other population-based algorithms, there still exist some problems it cannot solve very well. This paper presents an Enhanced Hybridized Artificial Bee Colony (EHABC) algorithm for optimization problems. The incentive mechanism of EHABC includes enhancing the convergence speed with the information of the global best solution in the onlooker bee phase and enhancing the information exchange between bees by introducing the mutation operator of Genetic Algorithm to ABC in the mutation bee phase. In addition, to enhance the accuracy performance of ABC, the opposition-based learning method is employed to produce the initial population. Experiments are conducted on six standard benchmark functions. The results demonstrate good performance of the enhanced hybridized ABC in solving continuous numerical optimization problems over ABC GABC, HABC and EABC.

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Zhang, Pei, Renyu Yang, Renhuan Yang, Gong Ren, Xiuzeng Yang, Chuangbiao Xu, Baoguo Xu, Huatao Zhang, Yanning Cai, and Yaosheng Lu. "Parameter estimation for fractional-order chaotic systems by improved bird swarm optimization algorithm." International Journal of Modern Physics C 30, no. 11 (November 2019): 1950086. http://dx.doi.org/10.1142/s0129183119500864.

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The essence of parameter estimation for fractional-order chaotic systems is a multi-dimensional parameter optimization problem, which is of great significance for implementing fractional-order chaos control and synchronization. Aiming at the parameter estimation problem of fractional-order chaotic systems, an improved algorithm based on bird swarm algorithm is proposed. The proposed algorithm further studies the social behavior of the original bird swarm algorithm and optimizes the foraging behavior in the original bird swarm algorithm. This method is applied to parameter estimation of fractional-order chaotic systems. Fractional-order unified chaotic system and fractional-order Lorenz system are selected as two examples for parameter estimation systems. Numerical simulation shows that the algorithm has better convergence accuracy, convergence speed and universality than bird swarm algorithm, artificial bee colony algorithm, particle swarm optimization and genetic algorithm.

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38

Villuendas-Rey, Yenny, Eley Barroso-Cubas, Oscar Camacho-Nieto, and Cornelio Yáñez-Márquez. "A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms." Mathematics 9, no. 7 (April 6, 2021): 786. http://dx.doi.org/10.3390/math9070786.

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Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

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39

Zhang, Bo Ping, and Guo Qing Li. "A Research of Improved Artificial Bee Colony Algorithm." Applied Mechanics and Materials 303-306 (February 2013): 1373–78. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1373.

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This paper studies an improved artificial bee colony algorithm, and two problems have been solved when the artificial colony algorithm is applied to objective optimization: the problem of slow convergence and premature aging problem. When the improved artificial bee colony algorithm is applied to land resources optimization problems, studies show the following two points. First, compared with the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, artificial bee colony algorithm has better adaptability and robustness in solving multivariate and multi peak global optimization problems. Second, compared with artificial bee colony algorithm, the improved artificial bee colony algorithm converges faster, the overall fitness increases by 8.9%, the maximum error is no more than 1%, and the short and medium term optimization has a high precision.

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ALAM, MD SHAFIUL, MD MONIRUL ISLAM, and KAZUYUKI MURASE. "ARTIFICIAL BEE COLONY ALGORITHM WITH IMPROVED EXPLORATIONS: A NOVEL APPROACH FOR NUMERICAL OPTIMIZATION." International Journal of Computational Intelligence and Applications 13, no. 02 (June 2014): 1450010. http://dx.doi.org/10.1142/s1469026814500102.

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The Artificial Bee Colony (ABC) algorithm is a recently introduced swarm intelligence algorithm that has been successfully applied on numerous and diverse optimization problems. However, one major problem with ABC is its premature convergence to local optima, which often originates from its insufficient degree of explorative search capability. This paper introduces ABC with Improved Explorations (ABC-IX), a novel algorithm that modifies both the selection and perturbation operations of the basic ABC algorithm in an explorative way. First, an explorative selection scheme based on simulated annealing allows ABC-IX to probabilistically accept both better and worse candidate solutions, whereas the basic ABC can accept better solutions only. Second, a self-adaptive strategy enables ABC-IX to automatically adapt the perturbation rate, separately for each candidate solution, to customize the degree of explorations and exploitations around it. ABC-IX is evaluated on several benchmark numerical optimization problems and results are compared with a number of state-of-the-art evolutionary and swarm intelligence algorithms. Results show that ABC-IX often performs better optimization than most other algorithms in comparison on most of the problems.

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41

Zhang, Libin, Lele Zhang, and Hongying Shan. "Evaluation of equipment maintenance quality: A hybrid multi-criteria decision-making approach." Advances in Mechanical Engineering 11, no. 3 (March 2019): 168781401983601. http://dx.doi.org/10.1177/1687814019836013.

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Maintenance plays a crucial role in the entire life cycle of equipment. With the acceleration of industrialization, the evaluation of equipment maintenance quality has undoubtedly become more challenging due to the complex mechanical structure, various maintenance modes, and so on. In order to make decisions scientifically, a hybrid multi-criteria decision-making approach integrating triangle fuzzy number, λ-fuzzy measure, TOPSIS, and Choquet fuzzy integral is proposed in this article. First, the interaction among criteria can be handled reasonably by fuzzy integral based on λ-fuzzy-measure. Second, fuzzy numbers which are given by experts are applied to deal with fuzzy linguistic value. In addition, artificial bee colony algorithm is first introduced to identify λ-fuzzy-measure. The comparison results of three optimization algorithms which include artificial bee colony algorithm, genetic algorithm, and particle swarm optimization prove artificial bee colony algorithm is more effective than genetic algorithm and particle swarm optimization. A case study which contains six maintenance alternatives is practiced to prove the effectiveness of the proposed hybrid multi-criteria decision-making approach. Finally, the comparison is made between the proposed method and two classical multi-criteria decision-making approaches which refer to TOPSIS and gray correlation, and the results demonstrate the proposed method is suitable to solve maintenance quality evaluation problem.

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Irsalinda, Nursyiva, and Sugiyarto Surono. "MODIFIKASI BARU ALGORITMA KOLONI LEBAH BUATAN UNTUK MASALAH OPTIMASI GLOBAL." Jurnal Ilmiah Matematika dan Pendidikan Matematika 10, no. 1 (June 29, 2018): 17. http://dx.doi.org/10.20884/1.jmp.2018.10.1.2833.

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Artificial Bee Colony (ABC) algorithm is one of metaheuristic optimization technique based on population. This algorithm mimicking honey bee swarm to find the best food source. ABC algorithm consist of four phases: initialization phase, employed bee phase, onlooker bee phase and scout bee phase. This study modify the onlooker bee phase in selection process to find the neighborhood food source. Not all food sources obtained are randomly sought the neighborhood as in ABC algorithm. Food sources are selected by comparing their objective function values. The food sources that have value lower than average value in that iteration will be chosen by onlooker bee to get the better food source. In this study the modification of this algorithm is called New Modification of Artificial Bee Colony Algorithm (MB-ABC). MB-ABC was applied to 4 Benchmark functions. The results show that MB-ABC algorithm better than ABC algorithm

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43

Ashrafinia, Saeed, Muhammad Naeem, and Daniel Lee. "Discrete Artificial Bee Colony for Computationally Efficient Symbol Detection in Multidevice STBC MIMO Systems." Advances in Artificial Intelligence 2013 (February 24, 2013): 1–14. http://dx.doi.org/10.1155/2013/578710.

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A Discrete Artificial Bee Colony (DABC) is presented for joint symbol detection at the receiver in a multidevice Space-Time Block Code (STBC) Mutli-Input Multi-Output (MIMO) communication system. Exhaustive search (maximum likelihood detection) for finding an optimal detection has a computational complexity that increases exponentially with the number of mobile devices, transmit antennas per mobile device, and the number of bits per symbol. ABC is a new population-based, swarm-based Evolutionary Algorithms (EA) presented for multivariable numerical functions and has shown good performance compared to other mainstream EAs for problems in continuous domain. This algorithm simulates the intelligent foraging behavior of honeybee swarms. An enhanced discrete version of the ABC algorithm is presented and applied to the joint symbol detection problem to find a nearly optimal solution in real time. The results of multiple independent simulation runs indicate the effectiveness of DABC with other well-known algorithms previously proposed for joint symbol detection such as the near-optimal sphere decoding, minimum mean square error, zero forcing, and semidefinite relaxation, along with other EAs such as genetic algorithm, estimation of distributions algorithm, and the more novel biogeography-based optimization algorithm.

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Wang, Wei Ping, Miao Yang, Tao Li, and Ying Qin. "Research on the Optimization Scheme of Base Station Construction Based on Artificial Bee Colony Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 3612–15. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3612.

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Artificial bee colony algorithm is a new meta heuristic bionic algorithm.Each bee can be viewed as an agent in the algorithm.Through the swarm synergies between the individual achieve the effect of swarm intelligence.Based on the analysis on the principle of bees gather honey,the process of solving functional optimization is converted into the process of swarm finding rich food sources,then we can solve the function.With the arrival of 4G Era,Base station construction is the most crucial step of the mobile communication network popularization, its significance and role should not be underestimated.How to realize the optimization of communication base station construction costs has been the research focus of the mobile service operator and colleges and universities.The artificial bee colony algorithm is applied to solve the optimal solution of base station construction scheme in this paper,which is very critical reference for decision in real base station construction.

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45

Liu, Wen. "A Multistrategy Optimization Improved Artificial Bee Colony Algorithm." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/129483.

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Анотація:

Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.

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46

Jiang, Shuo. "Dynamic Function Optimization by Improved Artificial Bee Colony Algorithm." Applied Mechanics and Materials 556-562 (May 2014): 3562–66. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3562.

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In this paper, an improved artificial bee colony algorithm (IABC) for dynamic environment optimization has been proposed. As we compared the IABC with greedy algorithm (GA), Particle swarm optimization (PSO) and original artificial bee colony algorithm (ABC), the result of dynamic function optimization shows that the IABC can obtain satisfactory solutions and good tracing performance for dynamic function in time.

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Alihodzic, Adis, and Milan Tuba. "Improved Bat Algorithm Applied to Multilevel Image Thresholding." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/176718.

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Анотація:

Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed.

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Wang, Jie-sheng, Shu-xia Li, and Jiang-di Song. "Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization." Computational Intelligence and Neuroscience 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/374873.

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In order to improve convergence velocity and optimization accuracy of the cuckoo search (CS) algorithm for solving the function optimization problems, a new improved cuckoo search algorithm based on the repeat-cycle asymptotic self-learning and self-evolving disturbance (RC-SSCS) is proposed. A disturbance operation is added into the algorithm by constructing a disturbance factor to make a more careful and thorough search near the bird’s nests location. In order to select a reasonable repeat-cycled disturbance number, a further study on the choice of disturbance times is made. Finally, six typical test functions are adopted to carry out simulation experiments, meanwhile, compare algorithms of this paper with two typical swarm intelligence algorithms particle swarm optimization (PSO) algorithm and artificial bee colony (ABC) algorithm. The results show that the improved cuckoo search algorithm has better convergence velocity and optimization accuracy.

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49

Xu, Yunfeng, Ping Fan, and Ling Yuan. "A Simple and Efficient Artificial Bee Colony Algorithm." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/526315.

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Анотація:

Artificial bee colony (ABC) is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC) algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

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Su, Guo Shao, Kun Qian, and Yan Zhang. "Load Distribution Calculation of Pile Group Using Artificial Bee Colony Algorithm." Applied Mechanics and Materials 204-208 (October 2012): 4878–83. http://dx.doi.org/10.4028/www.scientific.net/amm.204-208.4878.

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Анотація:

Artificial bee colony algorithm (ABC) is a newly swarm intelligence optimization algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. The results of performances testing using three benchmark functions show that the numbers of evaluation for fitness function of ABC are obviously less than that using particle swarm optimization algorithm. Thus, ABC has better suitability for solving multi-modal optimization problems. Finally, ABC algorithm is applied to the load distribution calculation of pile group. The result shows that the ABC is feasible and has the advantages of high efficiency and easy implementation

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