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Abstract

Metaheuristic algorithms are popular for solving engineering optimization problems because of their robustness and simplicity. However, they are computationally inefficient and can have difficulties find-ing optimal solutions in highly-constrained problems. This paper presents the benefits of a novel constraint-handling approach to improve multi-objective optimization with genetic algorithm. High-performing designs that violate constraints are repaired based on other designs created in the optimization process. Case study in-volves midship section of a 40,000 dwt tanker, where structural weight and deck adequacy are optimized. The approach is implemented into genetic algorithm called NSGA-II. The modified algorithm discovers more competitive designs with larger spread of the trade-off frontier. The modified algorithm outperforms the orig-inal in every iteration of the search. The benefits are pronounced especially in the beginning of the optimiza-tion, which is very useful for real-life design where optimization might be allowed to run for only a limited number of iterations.