AN IMPROVEMENT OF HYBRID WHALE OPTIMIZATION ALGORITHM

Özet

The difficulty in solving engineering problems creates difficulties in the selection of the methods to be used. Nature-inspired herd intelligence-based meta-heuristic optimization techniques have recently become the most popular algorithms for solving such problems. In this work, a new hybrid algorithm model has been developed to adapt to various problems. The developed models were adapted to 23 Benchmark test problems in the literature and compared with meta-heuristic algorithms. The algorithms aim to balance the optimization processes of exploration and exploitation. In the development of a meta-heuristic algorithm, it is very difficult to achieve a balance due to its stochastic structure. In this study, the new hybrid model improved by Multi-Verse Optimization (MVO) on the Sine Cosine Whale Optimization Algorithm (SCWOA) hybrid model, which is available in the literature, has increased the success of test problems. Although the SCWOA hybrid balances exploitation and exploration, the MVSCWOA (Multi-Verse Sine Cosine Whale Optimization Algorithm) hybrid algorithm, which was modified by modifying MVO's wormhole existence probability (WEP) and traveling distance rate (TDR), has succeeded in improving this balance further.