Self-Adaptive Constrained Artificial Bee Colony For Constrained Numerical Optimization

The Artificial Bee Colony (ABC) algorithm is a simple and effective swarm intelligentoptimization algorithm and has been successfully applied to solve a wide range of real-worldoptimization problem, such as Integer Planning, Multi-objective Planning, Picture Processing,Combinatorial Optimization, Numerical Function Optimization, and others. In this paper, wewill use the artificial bee colony to solve the constrained optimization problem.In this paper, we propose a self-adaptive constrained artificial bee colony (SACABC) forconstrained numerical optimization based on feasible rule method and multi-objectiveoptimization method. During the employment colony phase, the employed bee colony seversas the global search engine for each population based on feasible rule; During the onlookerbee colony phase, the algorithm explore the new search space based on multi-objectiveoptimization model. The feasible rule is simplicity and flexibility, which makes the feasiblerule can couple with any sort of selection mechanism. For the multi-objective optimization,the main idea of this method can convert constrain optimization to unconstrainedmuti-objective optimization. The method can maintain the good infeasible solution to avoidthe algorithm into the optimal solution. Therefore，in this paper, the algorithm combines twoconstrain methods. In addition, inspired by the differential evolution algorithm, two newsearch mechanisms are proposed to enhance the search ability and maintain populationdiversity. In order to enhance the convergence rate of the proposed algorithm, a self-adaptivemodification rate is used to make the algorithm can change many parameters. The value ofmodified rate is used can change according to the record of recent successful updateprobability and uses them to guide the generation of new modified rate. In order to verify theeffectiveness and efficiency of the algorithm, we selected24benchmark test functions from2006IEEE congress on Evolution Computation (CEC2006). The experimental results showthat the proposed algorithm better than, or at least comparable to, state-of-the-art approachesin terms of the quality of the resulting solutions from literature.