Development of an enhanced scatter search algorithm using discrete chaotic Arnold’s cat map

Solving optimization problems is an ever-growing subject with enormous number of algorithms. Examples such algorithms are Scatter Search (SS) and genetic Modifying improving can be done by adding diversity guidance to them. Chaotic maps quite sensitive the initial point, which means even a very slight change in value point would result dramatic sequence produced chaotic map Arnold's Cat Map. Map technique that provides long non-repetitive random-like sequences. play important role evolutionary meta-heuristics avoiding local optima speeding up convergence. This paper proposes implementation scatter search algorithm travelling salesman as case study, then implements compares developed hyper (SACMS) method against traditional Algorithm. SACMS Algorithm shows promising results decreasing iterations required get optimal solution(s). Travelling Salesman Problem, popular well-known example, implemented this demonstrate modified (SACMS). Implementation both same parameters: population size, cities, maximum iterations, reference set etc. The show improvement terms SS iteration reduction 10–46 % improvements time obtain solutions 65