Incremental Approximation Models for Constrained Evolutionary Optimization

Many real-world scientific and engineering problems are constrained optimization problems (COPs). To solve these problems, a variety of evolutionary algorithms have been proposed by incorporating different constraint-handling techniques. However, many of them have difficulties in achieving the global optimum due to the presence of highly constrained feasible regions in the search space. To effectively address the low degree of feasibility, this chapter presents an incremental approximation strategy-assisted constraint-handling method in combination with a multi-membered evolution strategy. In the proposed approach, we generate an approximate model for each constrained function with increasing accuracy, from a linear-type approximation to a model that has a complexity similar to the original constraint functions, thereby manipulating the complexity of the feasible region. Thanks to this property, our constrained evolutionary optimization algorithm can acquire the optimal solution conceivably. Simulations are carried out to compare the proposed algorithm with well-known references on 13 benchmark problems and three engineering optimization problems. Our computational results demonstrate that the proposed algorithm is comparable or superior to the state of the art on most of the test problems used in this study and a spring design optimization problem.

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