A Hybrid Algorithm Based on Bat-Inspired Algorithm and Differential Evolution for Constrained Optimization Problems

How to solve constrained optimization problems (COPs) is a significant research issue and we combine the bat-inspired algorithm (BA) with differential evolution (DE) into a new hybrid algorithm called BA-DE for solving the COPs. Traditional BAs are prone to sink into stagnation or local optima when no bat individual founds a better location than the past locations for several generations. DE is adopted for updating the past location of bat individuals to force BA to jump out of stagnation or local optima, since it has a great local searching capability. The performance of BA-DE algorithm is improved by the proposed hybrid mechanism. We use 24 well-known benchmark functions to verify the overall performance of our proposed algorithm. Comparisons show that BA-DE outperforms most advanced methods in terms of the final solution's quality.

[1]  Andries Petrus Engelbrecht,et al.  Empirical analysis of self-adaptive differential evolution , 2007, Eur. J. Oper. Res..

[2]  Yong Wang,et al.  An improved (μ + λ)-constrained differential evolution for constrained optimization , 2013, Inf. Sci..

[3]  Zbigniew Michalewicz,et al.  Test-case generator for nonlinear continuous parameter optimization techniques , 2000, IEEE Trans. Evol. Comput..

[4]  Xiaodong Li,et al.  Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization , 2014, IEEE Transactions on Evolutionary Computation.

[5]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[6]  Meie Shen,et al.  A Differential Evolution Algorithm With Dual Populations for Solving Periodic Railway Timetable Scheduling Problem , 2013, IEEE Transactions on Evolutionary Computation.

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

[8]  Marco Dorigo,et al.  Ant colony optimization for continuous domains , 2008, Eur. J. Oper. Res..

[9]  Mirjana Cangalovic,et al.  General variable neighborhood search for the continuous optimization , 2006, Eur. J. Oper. Res..

[10]  Lixin Tang,et al.  An Improved Differential Evolution Algorithm for Practical Dynamic Scheduling in Steelmaking-Continuous Casting Production , 2014, IEEE Transactions on Evolutionary Computation.

[11]  Matjaz Depolli,et al.  Asynchronous Master-Slave Parallelization of Differential Evolution for Multi-Objective Optimization , 2013, Evolutionary Computation.

[12]  Ying-Han Chen,et al.  Wall-Following Control of a Hexapod Robot Using a Data-Driven Fuzzy Controller Learned Through Differential Evolution , 2015, IEEE Transactions on Industrial Electronics.

[13]  Leandro dos Santos Coelho,et al.  Coevolutionary Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Julian Togelius,et al.  Geometric Differential Evolution for Combinatorial and Programs Spaces , 2013, Evolutionary Computation.

[15]  Carlos A. Coello Coello,et al.  A simple multimembered evolution strategy to solve constrained optimization problems , 2005, IEEE Transactions on Evolutionary Computation.

[16]  Yong Wang,et al.  Constrained Evolutionary Optimization by Means of ( + )-Differential Evolution and Improved Adaptive Trade-Off Model , 2011, Evolutionary Computation.

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

[18]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[19]  Faa-Jeng Lin,et al.  Wavelet Fuzzy Neural Network With Asymmetric Membership Function Controller for Electric Power Steering System via Improved Differential Evolution , 2015, IEEE Transactions on Power Electronics.

[20]  Yong Wang,et al.  A Multiobjective Optimization-Based Evolutionary Algorithm for Constrained Optimization , 2006, IEEE Transactions on Evolutionary Computation.

[21]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[22]  Shu-Mei Guo,et al.  Enhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator , 2015, IEEE Transactions on Evolutionary Computation.

[23]  Ning Dong,et al.  An unbiased bi-objective Optimization Model and Algorithm for constrained Optimization , 2014, Int. J. Pattern Recognit. Artif. Intell..

[24]  Yibo Hu Hybrid-Fitness Function Evolutionary Algorithm Based on Simplex Crossover and PSO Mutation for Constrained Optimization Problems , 2009, Int. J. Pattern Recognit. Artif. Intell..

[25]  Ming Yang,et al.  Differential Evolution With Auto-Enhanced Population Diversity , 2015, IEEE Transactions on Cybernetics.

[26]  Xin Yao,et al.  Stochastic ranking for constrained evolutionary optimization , 2000, IEEE Trans. Evol. Comput..

[27]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[28]  N. Hansen,et al.  Markov Chain Analysis of Cumulative Step-Size Adaptation on a Linear Constrained Problem , 2015, Evolutionary Computation.

[29]  Ling Wang,et al.  An effective co-evolutionary differential evolution for constrained optimization , 2007, Appl. Math. Comput..

[30]  A. Amirjanov The development of a changing range genetic algorithm , 2006 .

[31]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[32]  Carlos A. Coello Coello,et al.  THEORETICAL AND NUMERICAL CONSTRAINT-HANDLING TECHNIQUES USED WITH EVOLUTIONARY ALGORITHMS: A SURVEY OF THE STATE OF THE ART , 2002 .