Boundary Searching Genetic Algorithm: A Multi-objective Approach for Constrained Problems
The Performance of most of the Nature-/bio-inspired optimization algorithms is severely affected when applied for solving constrained problems. The approach of Genetic Algorithm (GA) is one of the most popular techniques; however, similar to other contemporary algorithms, its performance may also degenerate when applied for solving constrained problems. There are several constraint handling techniques proposed so far; however, developing efficient constraint handling technique still remains a challenge for the researchers. This paper presents a multi-objective optimization approach referred to as Boundary Searching GA (BSGA). It considers every constraint as an objective function and focuses on locating boundary of the feasible region and further search for the optimum solution. The approach is validated by solving four test problems. The solutions obtained are very competent in terms of the best and mean solutions in comparison with contemporary algorithms. The results also demonstrated its robustness solving these problems. The advantages, limitations and future directions are also discussed.
KeywordsBoundary Searching Genetic Algorithm Constrained Optimization Multi-Objective Optimization
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