An Improvement Of Hybrid Whale Optimization Algorithm

The difficulty in solving engineering problems creates difficulties in the selection of the methods to beused. Nature-inspired herd intelligence-based meta-heuristic optimization techniques have recentlybecome the most popular algorithms for solving such problems. In this work, a new hybrid algorithmmodel has been developed to adapt to various problems. The developed models were adapted to 23Benchmark test problems in the literature and compared with meta-heuristic algorithms. Thealgorithms aim to balance the optimization processes of exploration and exploitation. In thedevelopment of a meta-heuristic algorithm, it is very difficult to achieve a balance due to its stochasticstructure. In this study, the new hybrid model improved by Multi-Verse Optimization (MVO) on theSine Cosine Whale Optimization Algorithm (SCWOA) hybrid model, which is available in theliterature, has increased the success of test problems. Although the SCWOA hybrid balancesexploitation and exploration, the MVSCWOA (Multi-Verse Sine Cosine Whale OptimizationAlgorithm) hybrid algorithm, which was modified by modifying MVO's wormhole existenceprobability (WEP) and traveling distance rate (TDR), has succeeded in improving this balance further.WEP is used instead of parameter, which determines the update direction in SCA, and TDR is usedin place of (varies between -1 and -2) used in the update of l, which is the inter-elementmultiplication parameter in WOA. The results obtained from the newly developed hybrid model haveshown that it makes the search and exploitation feature more effective by showing better results thanSCWOA, WOA, SCA, and MVO. MVSCWOA was successful in test problems.Keywords: Benchmark, Sine Cosine Algorithm, Whale Optimization Algorithm, Multi-VerseOptimization