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An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. (English) Zbl 1219.90129
Summary: We explore the capabilities of different types of evolution strategies (ES) to solve global optimization problems with constraints. The aim is to highlight the idea that the selection of the search engine is more critical than the selection of the constraint-handling mechanism, which can be very simple indeed. We show how using just three simple comparison criteria based on feasibility, the simple evolution strategy can be led to the feasible region of the search space and find the global optimum solution (or a very good approximation of it). Different ES including a variation of a \((\mu +1)\)-ES and \((\mu^+ \lambda)\)-ES with or without correlated mutation were implemented. Such approaches were tested using a well-known test suite for constrained optimization. Furthermore, the most competitive version found (among those five) was compared against three state-of-the-art approaches and it was also compared against a GA using the same constraint-handling approach. Finally, our evolution strategy was used to solve some engineering design problems.

MSC:
90C26 Nonconvex programming, global optimization
90C59 Approximation methods and heuristics in mathematical programming
Software:
Genocop
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