Engineering design optimization using an improved local search based epsilon differential evolution algorithm

Many engineering problems can be categorized into constrained optimization problems (COPs). The engineering design optimization problem is very important in engineering industries. Because of the complexities of mathematical models, it is difficult to find a perfect method to solve all the COPs very well. $$\varepsilon $$ε constrained differential evolution ($$\varepsilon $$εDE) algorithm is an effective method in dealing with the COPs. However, $$\varepsilon $$εDE still cannot obtain more precise solutions. The interaction between feasible and infeasible individuals can be enhanced, and the feasible individuals can lead the population finding optimum around it. Hence, in this paper we propose a new algorithm based on $$\varepsilon $$ε feasible individuals driven local search called as $$\varepsilon $$ε constrained differential evolution algorithm with a novel local search operator ($$\varepsilon $$εDE-LS). The effectiveness of the proposed $$\varepsilon $$εDE-LS algorithm is tested. Furthermore, four real-world engineering design problems and a case study have been studied. Experimental results show that the proposed algorithm is a very effective method for the presented engineering design optimization problems.

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