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Socio-inspired Optimization Metaheuristics: A Review

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Socio-cultural Inspired Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 828))

Abstract

The chapter attempts to review the recent literature in the upcoming area of socio-inspired metaheuristics. These optimization methodologies are a novel subbranch of the popular Evolutionary algorithms under the class of nature-inspired algorithms for optimization. The socio-inspired class of algorithms seeks inspiration from human behavior seen during the course of the social and cultural interactions with others. A human being exhibits natural and inherent tendencies of competitive behavior, to collaborate, work together and interact socially and culturally. All such natural behaviors help an individual to learn and imbibe behaviors from other humans, resulting in them to adapt and improve their own behaviors in due course of time. This tendency observed in humans serves as a motivation for socio-inspired optimization algorithms were the agents in the optimizer algorithm work toward achieving some shared goals. This class of optimization algorithms finds their strength in the fact that individuals tend to adapt and evolve faster through interactions in their social setup than just through biological evolution based on inheritance alone. In the article, the authors introduce and summarize the existing socio-inspired algorithms, their sources of inspiration, and the basic functioning. Additionally, the review also sheds light on the limitations and the strengths of each of these socio-inspired optimizers discussed in the article. The problem domains to which these optimizers have been successfully applied to is also presented. The authors note that most of the algorithms developed in this subbranch of nature inspire methodologies in this area are new and are still evolving, thus promising scope of work in this domain.

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Authors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International University, Pune, 412115, MH, India

    Meeta Kumar & Anand J. Kulkarni

  2. Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada

    Anand J. Kulkarni

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  1. Meeta Kumar
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Correspondence to Anand J. Kulkarni .

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Editors and Affiliations

  1. Department of Mechanical Engineering, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India

    Anand J. Kulkarni

  2. ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, Madhya Pradesh, India

    Pramod Kumar Singh

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  4. Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

    Ali Husseinzadeh Kashan

  5. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Kang Tai

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Kumar, M., Kulkarni, A.J. (2019). Socio-inspired Optimization Metaheuristics: A Review. In: Kulkarni, A.J., Singh, P.K., Satapathy, S.C., Husseinzadeh Kashan, A., Tai, K. (eds) Socio-cultural Inspired Metaheuristics. Studies in Computational Intelligence, vol 828. Springer, Singapore. https://doi.org/10.1007/978-981-13-6569-0_12

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