In, a multi-objective version of JSA is proposed and it has been applied to solve multiple objective engineering problems. proposed an alternative method for estimating the parameters of the PEM fuel cell. For example, JSA has been used to determined the optimal solution of global benchmark functions in and its efficiency over other metaheuristic (MH) techniques has been established. In accordance with the characteristics of JSA, it has been applied to solve different sets of optimization problems. This algorithm emulates the behaviour of a jellyfish swarm in nature. Recently, a new swarm-based optimization technique has been proposed which named is named the Jellyfish Search Algorithm (JSA). Moreover, it achieved gained high performances compared to other competitors according to several standard evaluation measures, including fitness function, makespan, and energy consumption. The real-world application results demonstrated that DJSD is highly competent in dealing with challenging real applications. In addition, to further validate the performance of DJSD in solving real-world applications, experiments were conducted to tackle the task scheduling problem in cloud computing applications. The findings illustrated that the developed DJSD method achieved promising results, discovered new search regions, and found new best solutions. The results are compared with advanced well-known metaheuristic approaches. A comprehensive set of experiments is conducted using thirty classical benchmark functions to validate the effectiveness of the proposed DJSD method. The disruption operator is employed in the exploitation stage to boost the diversity of the candidate solutions throughout the optimization operation and avert the local optima problem. This combination is performed dynamically using a fluctuating parameter that represents the characteristics of a hammer. The developed DJSD method incorporates the Simulated Annealing operators into the conventional Jellyfish Search Algorithm in the exploration stage, in a competitive manner, to enhance its ability to discover more feasible regions. This paper presents a novel dynamic Jellyfish Search Algorithm using a Simulated Annealing and disruption operator, called DJSD.
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