Socio-inspired Optimization Metaheuristics: A Review
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 opt
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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.
M. Kumar · A. J. Kulkarni (B) Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, MH, India e-mail: [email protected]; [email protected] M. Kumar e-mail: [email protected] A. J. Kulkarni Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON N9B3P4, Canada © Springer Nature Singapore Pte Ltd. 2019 A. J. Kulkarni et al. (eds.), Socio-cultural Inspired Metaheuristics, Studies in Computational Intelligence 828, https://doi.org/10.1007/978-981-13-6569-0_12
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M. Kumar and A. J. Kulkarni
Keywords Socio-inspired algorithms · Optimization · Nature-inspired optimization
1 Introduction The article attempts to summarize the state of the art in the field of socio-inspired methodologies for optimization; also referred to as social algorithms (SA). To tackle complex and large scale optimization problems, metaheuristic methods have been a popular choice for researchers since decades [10, 57]. These approximate methods are prevalent owing to their simplistic algorithmic design, their lucid iterative nature and a generic framework which can be adapted to solve a wide variety of optimization problems with little changes to their framework. This makes metaheuristics more effective and efficient in comparison to their
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