Nature inspired metaheuristic optimization is a very active field of research. In this thesis I conducted an overview of nature inspired metaheuristics and made a comparison based on their features. I chose two very similar algorithms (Grey wolf optimizer and Whale optimization algorithm), made a detailed analysis of each one and a detailed comparison between the two. In the comparison I highlighted the key similarities and differences of both approaches which I then carried into the hybridization of the two. I implemented one hybrid for each of the algorithms. The hybrids contained mechanisms for optimizations from the other algorithm. Testing was done on well known test functions for optimization. In the results I noticed a general degradation of performance for the hybrid algorithms. I concluded that the different optimization mechanisms work with varying efficiencies for the different test functions and most of the time do not mix well. The presence of a different optimization mechanism from another algorithm can degrade performance in some cases, may improve it in others, where the original performs poorly, or may cause a significant degradation where the original performs well.
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