Smart flight and dynamic tolerances in the artificial bee colony for constrained optimization

This paper presents an adaptation of a novel algorithm based on the foraging behavior of honey bees to solve constrained numerical optimization problems. The modifications focus on improving the way the feasible region is approached by using a new operator which allows the generation of search directions biased by the best solution so far. Furthermore, two dynamic tolerances applied in the constraint handling mechanism help the algorithm to the generation of feasible solutions. The approach is tested on a set of 24 benchmark problems and its behavior is compared against the original algorithm and with respect to some state-of-the-art algorithms.

[1]  D. Pham,et al.  THE BEES ALGORITHM, A NOVEL TOOL FOR COMPLEX OPTIMISATION PROBLEMS , 2006 .

[2]  Jing J. Liang,et al.  Problem Deflnitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization , 2006 .

[3]  Yuren Zhou,et al.  Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Tetsuyuki Takahama,et al.  Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[5]  Marc Schoenauer,et al.  ASCHEA: new results using adaptive segregational constraint handling , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Hussein A. Abbass,et al.  MBO: marriage in honey bees optimization-a Haplometrosis polygynous swarming approach , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[8]  Efrén Mezura-Montes,et al.  Improved Particle Swarm Optimization in Constrained Numerical Search Spaces , 2009, Nature-Inspired Algorithms for Optimisation.

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Jeng-Shyang Pan,et al.  Enhanced Artificial Bee Colony Optimization , 2022 .

[11]  Omid Bozorg Haddad,et al.  Honey-Bees Mating Optimization (HBMO) Algorithm: A New Heuristic Approach for Water Resources Optimization , 2006 .

[12]  Haiyan Lu,et al.  Self-adaptive velocity particle swarm optimization for solving constrained optimization problems , 2008, J. Glob. Optim..

[13]  Debasish Ghose,et al.  Glowworm swarm based optimization algorithm for multimodal functions with collective robotics applications , 2006, Multiagent Grid Syst..

[14]  D. Karaboga,et al.  Artificial Bee Colony ( ABC ) , Harmony Search and Bees Algorithms on Numerical Optimization , 2009 .

[15]  Gary G. Yen,et al.  A Self Adaptive Penalty Function Based Algorithm for Constrained Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[16]  Lale Özbakır,et al.  Artificial Bee Colony Algorithm and Its Application to Generalized Assignment Problem , 2007 .

[17]  Carlos A. Coello Coello,et al.  A bi-population PSO with a shake-mechanism for solving constrained numerical optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[18]  Xin-She Yang,et al.  Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms , 2005, IWINAC.

[19]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[20]  H A Abbass,et al.  MARRIAGE IN HONEY-BEE OPTIMIZATION (MBO): A HAPLOMETROSIS POLYGYNOUS SWARMING APPROACH , 2001 .

[21]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[22]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[23]  Kevin D. Seppi,et al.  Linear equality constraints and homomorphous mappings in PSO , 2005, 2005 IEEE Congress on Evolutionary Computation.

[24]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[25]  Fernando Buarque de Lima Neto,et al.  Fish School Search , 2021, Nature-Inspired Algorithms for Optimisation.

[26]  Yuren Zhou,et al.  An Adaptive Tradeoff Model for Constrained Evolutionary Optimization , 2008, IEEE Transactions on Evolutionary Computation.

[27]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[28]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[29]  Carlos A. Coello Coello,et al.  Identifying on-line behavior and some sources of difficulty in two competitive approaches for constrained optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[30]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[31]  Zbigniew Michalewicz,et al.  Evolutionary Algorithms for Constrained Parameter Optimization Problems , 1996, Evolutionary Computation.

[32]  Efrén Mezura-Montes,et al.  Self-adaptive and Deterministic Parameter Control in Differential Evolution for Constrained Optimization , 2009 .