Artificial Bee Colony Algorithm And Differential Evolutionary Algorithm Based On New Ensemble Of Constraint Handing Techniques

Many practical optimization problems involve inequality and equal-ity constraints.Over the past several decades,several constraint pro-cessing techniques have been developed for evolutionary algorithm-s(EAs)by many scholars.According to the no free lunch theorem,a single constraint processing technology can't be superior to all oth-er techniques in every problem.Influenced by many factors,such as the ratio between feasible region and the entire search space,the multi In other words-modality of the problem,the selection of EA,and the overall exploration/local exploitation phase,the various con-straint handling techniques are valid at various stages in the search process.Therefore,Mallipeddi et al.proposed ensemble of constraint handling techniques(ECHT)to solve the optimization problem with constraints.In this paper,by improving the integration of constraint processing technology proposed by Mallipeddi,two new ensemble of constraint handling techniques are proposed,combined with artificial bee colony algorithm and differential evolution algorithm,respective-ly.The first algorithm is an artificial bee colony algorithm based on ensemble of constraint handing techniques(ECHTABC).A new constraint handing technique is added to Mallipeddi's ECHT.These five constraint handing techniques are combined into two ensembles of constraint handing techniques,which are used in employed bees phase and onlooker bees phase in ABC algorithm.Performance of ECHTABC has been tested on 28 benchmark functions in CEC 2017 and four classical engineering design problems.Experimental results show that ECHTABC can effectively improve the accuracy of solu-tion,It is more competitive than other state-of-the-art constrained optimization algorithms.The second algorithm is a differential evolutionary algorithm based on new ensemble of constraint handing techniques(NECHTDE).At the stage of generating new individuals,the algorithm adopts three different mutation strategies.Different constraint handing techniques are used to select new individuals,and local search is introduced to enhance the local optimization ability of the algorithm.Numerical experiments are carried out on 28 benchmark functions in CEC 2017 and the new algorithm is compared with several other advanced algo-rithms.The experiment results show that the NECHTDE algorithm performs better in solution accuracy.