Abstract
Nature-inspired algorithms in computer science and engineering are algorithms that take their inspiration from living things and imitate their actions in order to construct functional models. The SOS algorithm (symbiotic organisms search) is a new promising metaheuristic algorithm. It is based on the symbiotic relationship that exists between different species in an ecosystem. Organisms develop symbiotic bonds like mutualism, commensalism, and parasitism to survive in their environment. Standard SOS has since been modified several times, either by hybridization or as better versions of the original algorithm. Most of these modifications came from engineering construction works and other discipline like medicine and finance. However, little improvement on the standard SOS has been noticed on its application in cloud computing environment, especially cloud task scheduling. As a result, this paper provides an overview of SOS applications in task scheduling problem and suggest a new enhanced method for better performance of the technique in terms of fast convergence speed.
Highlights
The term "cloud computing" refers to a fully centralized, scalable, on-demand computing network with distributed virtual infrastructure, storage, and pay-per-use services[1][2][3]
In another study, [35] presents an efficient search algorithm for a multi-objective task scheduling problems based on symbiotic organisms search with a chaotic optimization strategy in a cloud computing environment
Metaheuristic research around the world has resulted in optimization techniques that have proved to be superior to conventional gradient-based approaches
Summary
The term "cloud computing" refers to a fully centralized, scalable, on-demand computing network with distributed virtual infrastructure, storage, and pay-per-use services[1][2][3]. Symbiotic Organisms Search algorithm (SOS) is an SI(Swarm Intelligence)-based recently developed metaheuristic algorithm that was inspired by nature[19]. When two species form a relationship in which one benefits and the other hurts, it is referred to as parasitism Both mutualism and commensalism operations focus on creating new species for the generation by allowing the search procedure to find solutions to the problems within the solution search space, thereby improving the algorithm's exploratory capability. This paper provides a comprehensive analysis of the standard SOS algorithm, its basic concepts and structures, variants, and hybrid implementations for addressing constrained, unconstrained, single objective, multiple objectives, large scale global optimization problems, and practical oriented real world optimization problems in a cloud computing setting. Three distinct search processes govern the possible solution, which are modeled after three basic symbiotic interactions: mutualism, commensalism, and parasitism
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