Abstract
Whale optimization algorithm is a new member of nature inspired optimization algorithm which is inspired from foraging behaviour of humpback whales. Similar to other heuristic algorithms, Whale optimization algorithm suffers with immature convergence and stagnation problems while solving optimization problems. In this paper, Whale optimization algorithm is hybridized with Laplace Crossover operator and a new algorithm, Laplacian whale optimization algorithm (LXWOA), has been proposed. It has been used to solve a set of 23 classical benchmark functions which consists of scalable unimodal functions, scalable multimodal functions and low dimensional multimodal functions and the results are compared with original whale optimization algorithm, particle swarm optimization, differential evolution, gravitational search algorithm and Laplacian gravitational search algorithm. In this paper, LXWOA and WOA have also been used to solve the problem of extraction of compounds from gardenia.
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