The basic concept of this algorithm is to mimic the concept of the ‘survival of the fittest’ it simulates the processes observed in a natural system where the strong tends to adapt and survive while the weak tends to perish. The Genetic Algorithm (GA) introduced by John Holland in 1975, is a search optimization algorithm based on the mechanics of the natural selection process. The conclusion section is presented at the end of this paper. From there, the two best performing algorithms are selected to investigate their variants performance against the best performing algorithm in five benchmark functions. The results are discussed comprehensively after that with statistical analysis in the following section.
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After that, an experiment is conducted to measure the performance of the considered algorithms on thirty benchmark functions. This paper outline starts with brief discussion on seven SI-based algorithms and is followed by general discussion on others available algorithms. This division allows the swarm to tackle complex problems that require individuals to work together.
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The second property of SI is division of labour which is defined as the simultaneous execution of various simple and feasible tasks by individuals. Multiple interactions occur when the swarms share information among themselves within their searching area.
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Fluctuations meanwhile are useful for randomness. Positive and negative feedbacks are useful for amplification and stabilization respectively. also stated that self-organization relies on four fundamental properties of positive feedback, negative feedback, fluctuations and multiple interactions. Self-organization is defined as the capability of a system to evolve its agents or components in to a suitable form without any external help. Two fundamental concepts that are considered as necessary properties of SI are self-organization and division of labour. Examples of SI include the group foraging of social insects, cooperative transportation, nest-building of social insects, and collective sorting and clustering. SI is the collective intelligence behaviour of self-organized and decentralized systems, e.g., artificial groups of simple agents. Bonabeau defined SI as “ The emergent collective intelligence of groups of simple agents”. Swarm Intelligence (SI) has attracted interest from many researchers in various fields. For more information, please go to The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. The Regional Growth Fund (RGF) is a $3.2 billion fund supporting projects and programmes which are using private sector investment to generate economic growth as well as creating sustainable jobs between now and the mid-2020s. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedĭata Availability: All relevant data are within the paper and its Supporting Information files.įunding: This research paper was supported by the GAMMA Programme which is funded through the Regional Growth Fund. Received: SeptemAccepted: FebruPublished: May 18, 2015Ĭopyright: © 2015 Ab Wahab et al. PLoS ONE 10(5):Īcademic Editor: Catalin Buiu, Politehnica University of Bucharest, ROMANIA
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Citation: Ab Wahab MN, Nefti-Meziani S, Atyabi A (2015) A Comprehensive Review of Swarm Optimization Algorithms.