Abstract
Metaheuristic optimization is the technique of finding the most suitable solution among the possible solutions for a particular problem. We encounter many problems in the real world, such as timetabling, path planning, packing, traveling salesman, trajectory optimization, and engineering design problems. The two main problems faced by all metaheuristic algorithms are being stuck in local optima and early convergence. To overcome these problems and achieve better performance, chaos theory is included in the metaheuristic optimization. The chaotic maps are employed to balance the exploration and exploitation efficiently and improve the performance of algorithms in terms of both local optima avoidance and convergence speed. The literature shows that chaotic maps can significantly boost the performance of metaheuristic optimization algorithms. In this chapter, chaos theory and chaotic maps are briefly explained. The use of chaotic maps in metaheuristic is presented, and an enhanced version of GSA with chaotic maps is shown as an application.
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