A hybrid genetic algorithm for constrained multi-objective optimization under uncertainty and target matching problems

• This paper proposed a hybrid genetic algorithm for optimization under bounded uncertainty.
• Local search is used for anti-optimization technique.
• The anti-optimization is achieved with Hooke and Jeeves method.
• Anti-optimization requires only a few additional percentage of total computation cost.
• The proposed algorithm has great potential for solving constrained multi-objective optimization problems under certainty.

This work presents a new approach for interval-based uncertainty analysis. The proposed approach integrates a local search strategy as the worst-case-scenario technique of anti-optimization with a constrained multi-objective genetic algorithm. Anti-optimization is a term for an approach to safety factors in engineering structures which is described as pessimistic and searching for least favorable responses, in combination with optimization techniques but in contrast to probabilistic approaches. The algorithm is applied and evaluated to be efficient and effective in producing good results via target matching problems: a simulated topology and shape optimization problem where a ‘target’ geometry set is predefined as the Pareto optimal solution and a constrained multiobjective optimization problem formulated such that the design solutions will evolve and converge towards the target geometry set.

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