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A New Bio-heuristic Hybrid Optimization for Constrained Continuous Problems

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Transactions on Computational Science XXXVIII

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 12620))


A novel bio-inspired evolutionary algorithm known as MoFAL is presented in this article. The proposed algorithm (MoFAL) is based on the hybrid amalgamation of two nature inspired methods based on Moth Flame Optimization and Ant Lion Optimizer algorithms. It is well known that elitism forms an important characteristic of evolutionary algorithms that allows them to maintain the best fitness(es) obtained at any stage of the optimization process. MoFal is bench-marked using a set of 23 classical benchmark functions employed to test different characteristics during its evolutionary computation process. Numerical experiments demonstrate that the solutions of the constrained optimization problems like Pressure Vessel and the Rolling Element Bearing designs found using our algorithm are highly accurate and their convergence is comparatively fast coupled with improved exploration, local optima avoidance and exploitation. The results clearly exhibit that MoFAL algorithm is capable of finding superior optimal designs for our case study problems that include diverse search spaces. Our algorithm is able to determine global solutions of constrained optimization problems more efficiently than traditional evolutionary algorithms, and also avoid the occurrence of premature phenomena during its convergence process.

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The work in this article is supported by the Optimization Problems Research and Application Laboratory (OPR-AL), Ryerson University and Natural Sciences and Engineering Research Council of Canada (NSERC). Also, we thank for the open source datasets provided by the original algorithm formulators at Mirjalili (2020a), Mirjalili (2020b) which assisted in our research experiments.

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Correspondence to Reza Sedaghat .

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Siddavaatam, P., Sedaghat, R. (2021). A New Bio-heuristic Hybrid Optimization for Constrained Continuous Problems. In: Gavrilova, M.L., Tan, C.K. (eds) Transactions on Computational Science XXXVIII. Lecture Notes in Computer Science(), vol 12620. Springer, Berlin, Heidelberg.

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