Dynamic Jellyfish Search Algorithm Based on Simulated Annealing and Disruption Operators for Global Optimization with Applications to Cloud Task Scheduling

This paper presents a novel dynamic Jellyfish Search Algorithm using Simulated Annealing and disruption operator, called DJSD. The developed DJSD method incorporates the operators into conventional in exploration stage, competitive manner, to enhance its ability discover more feasible regions. combination is performed dynamically fluctuating parameter that represents characteristics of hammer. operator employed exploitation stage boost diversity candidate solutions throughout optimization operation avert local optima problem. A comprehensive set experiments conducted thirty classical benchmark functions validate effectiveness proposed method. results are compared with advanced well-known metaheuristic approaches. findings illustrated achieved promising results, discovered new search regions, found best solutions. In addition, further performance solving real-world applications, were tackle task scheduling problem cloud computing applications. application demonstrated highly competent dealing challenging real Moreover, it gained high performances other competitors according several standard evaluation measures, including fitness function, makespan, energy consumption.