Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems

Abstract

A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony (ABC) algorithm with biogeography-based optimization (BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches.

Keywords

artificial bee colony / biogeography-based optimization / constrained optimization / mechanical design problem

References

[1] CAIZ-x, WANGYong. A multi-objective optimization-based evolutionary algorithm for constrained optimization [J]. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 658-675

[2] DANESHYARIM, YENG G. Constrained multiple-swarm particle swarm optimization within a cultural framework [J]. IEEE Transactions on Systems, Man and Cybernetics, 2012, 42(2): 475-490

[3] LONGW, LIANGX-m, HUANGY-f, CHENY-xiong. A hybrid differential evolution augmented Lagrangian method for constrained numerical and engineering optimization [J]. Computer-Aided Design, 2013, 45(12): 1562-1574

[4] KARABOGAD, AKAYB. A modified artificial bee colony (ABC) algorithm for constrained optimization problems [J]. Applied Soft Computing, 2011, 11(3): 3021-3031

[5] BOUSSAIDI, CHATTERJEEA, SIARRYP, AHMED-NACERM. Biogeography- based optimization for constrained optimization problems [J]. Computers & Operations Research, 2012, 39(12): 3293-3304

[6] BONYADIM R, LIX, MICHALEWICZZ. A hybrid particle swarm with a time-adaptive topology for constrained optimization [J]. Swarm and Evolutionary Computation, 2014, 18: 22-37

[7] ELSAYEDS M, SARKERR A, MEZURA-MONTESE. Self-adaptive mix of particle swarm methodologies for constrained optimization [J]. Information Sciences, 2014, 277: 216-233

[8] LONGW, ZHANGW-z, HUANGY-f, CHENY-xiang. A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization [J]. Journal of Central South University, 2014, 21(8): 3197-3204

[9] JIAG-b, WANGY, CAIZ-x, JINY-chu. An improved (µ+?)-constrained differential evolution for constrained optimization [J]. Information Sciences, 2013, 222: 302-322

[10] KARABOGAD. An idea based on honey bee swarm for numerical optimization [R]. Technical Report-TR06, Kayseri, Turkey: Erciyes University, 2005

[11] SIMOND. Biogeography-based optimization [J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6): 702-713

[12] DEBK. An efficient constraint handling method for genetic algorithms [J]. Computer Methods in Applied Mechanics and Engineering, 2000, 186: 311-338

[13] RUNARSSONT P, YAOX. Stochastic ranking for constrained evolutionary optimization [J]. IEEE Transactions on Evolutionary Computation, 2000, 4(3): 284-294

[14] MEZURA-MONTESE, CETINA-DOMINGUEZO. Empirical analysis of a modified artificial bee colony for constrained numerical optimization [J]. Applied Mathematics and Computation, 2012, 218(22): 10943-10973

[15] GANDOMIA H, YANGX S, TALATAHARIS, DEBS. Couple eagle strategy and differential evolution for unconstrained and constrained global optimization [J]. Computers and Mathematics with Applications, 2012, 63(1): 191-200

[16] LONGW, LIANGX-m, HUANGY-f, CHENY-xiong. An effective hybrid cuckoo search algorithm for constrained global optimization [J]. Neural Computation and Applications, 2014, 25: 911-926

[17] ROCHAA, FERNANDESE. Feasibility and dominance rules in the electromagnetism-like mechanism for global optimization [J]. Lecture Notes in Computer Science, 2008, 5071: 768-783

[18] ESKANDARH, SADOLLAHA, BAHREININEJADA, HAMDIM. Water cycle algorithm—A novel metaheuristic optimization method for solving constrained engineering optimization problems [J]. Computers and Structures, 2012, 110: 151-166

[19] KANNANB K, KRAMERS N. An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design [J]. Journal of Mechanical Design, 1994, 116(2): 405-411

[20] COELLOC A C. Use of a self-adaptive penalty approach for engineering optimization problems [J]. Computers in Industry, 2000, 41(2): 113-127