Artificial bee colony implementation for multimodal optimization problems

In real-world problems, many optimization problems may involve multiple high-quality global solutions. Such problems are called multimodal optimization problems. In such problems,it needs to be find all global optimums. Such problems are more difficult than single optimum problems and algorithms that perform searches in different regions of the search space are needed to find all optimums. The Artificial Bee Colony (ABC) algorithm is a population-based metaheuristic method inspired by the foraging behavior of honey bees and is used to solve continuous optimization problems. It is known that the ABC algorithm gives good results in problems with the single optimum. No methods have been found in the literature to solve ABC algorithms to multimodal optimization problems. In this thesis, ABC algorithm which can be used to solve multi-modal optimization problems has been developed. Algorithm performance is tested on the CEC 2015 multimodal benchmark function set. Algorithm performance is evaluated by comparing with similar methods in the literature.