A dual-population differential evolution with coevolution for constrained optimization.

Inspired by the fact that in modern society, team cooperation and the division of labor play important roles in accomplishing a task, this paper proposes a dual-population differential evolution (DPDE) with coevolution for constrained optimization problems (COPs). The COP is treated as a bi-objective optimization problem where the first objective is the actual cost or reward function to be optimized, while the second objective accounts for the degree of constraint violations. At each generation during the evolution process, the whole population is divided into two based on the solution's feasibility to treat the both objectives separately. Each subpopulation focuses on only optimizing the corresponding objective which leads to a clear division of work. Furthermore, DPDE makes use of an information-sharing strategy to exchange search information between the different subpopulations similar to the team cooperation. The comparison of the proposed method on a number of benchmark functions with selected state-of-the-art constraint-handling algorithms indicates that the proposed technique performs competitively and effectively.