Hybrid Bee Colony Algorithm with Whale Algorithm

Abstract

In this research paper, two hybrid algorithms of Meta-Heuristic algorithms, both of which are inspired by nature, are presented: the Bee- settlement Amendment algorithm (BCO) and the Whale Amendment Algorithm (WOA). The Bee settlement Amendment algorithm is an amendment algorithm founded on swarm intelligence modeling attitude. It is one of the techniques of synthetic information that focuses on studying the grouping conduct of decentralized systems, which are represented by groups of modest elements that react locally simultaneously and with the surrounding perimeter. One of the methods that distinguishes it is the method of exploration. As for the Whale Amendment Algorithm, whom represents the friendly conduct of the hump-back whale. It is based on the fancy net fishing design. One of the advantages of this algorithm depends on the poise between screening and utilization and avoidance of falling into local solutions. A hybridization process was carried out between the two algorithms, and the new algorithm was named (ABCWOA), and the whole algorithm was used to 16 rising -measurement Amendment assignment with diverse frequencies between (100, 200, 500, and 1000). The outcomes of the proposition algorithm showed access to optimality solutions by achieving the minimum value (f_min). For most assignment, the outcomes of this algorithm were disparity with the search algorithms.

Keywords

• Optimization
• Bee-Colony Algorithm
• Whale Optimization Algorithm
• Hybrid Algorithms

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