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Expectation Algorithm (ExA): A Socio-inspired Optimization Methodology

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Socio-cultural Inspired Metaheuristics

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

Abstract

This paper introduces a new socio-inspired algorithm referred to as Expectation Algorithm (ExA), which is mainly inspired from the society individuals. The ExA modelled the variables of the problems as individuals of a society. The variables select their values by expecting the values of the other variables minimizing the objective function. The performance of the algorithm is validated by solving 50 unconstrained test problems with dimensions up to 30. The solutions were compared with several recent algorithms such as Covariance Matrix Adaptation Evolution Strategy, Artificial Bee Colony, Comprehensive Learning Particle Swarm Optimization, Self-adaptive Differential Evolution Algorithm, Backtracking Search Optimization Algorithm, Ideology Algorithm and Multi-Cohort Intelligence algorithm. The Wilcoxon signed-rank test was carried out for the statistical analysis and verification of the performance. The results from this study highlighted that the ExA outperformed most of the other algorithms in terms of function evaluations and computational time. The prominent features of the ExA algorithm along with the limitations are discussed as well.

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Author information

Authors and Affiliations

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

    Apoorva S. Shastri, Aishwary Jagetia, Amit Sehgal, Meet Patel & Anand J. Kulkarni

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

    Anand J. Kulkarni

Authors
  1. Apoorva S. Shastri
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  2. Aishwary Jagetia
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  3. Amit Sehgal
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  4. Meet Patel
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  5. Anand J. Kulkarni
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Corresponding author

Correspondence to Apoorva S. Shastri .

Editor information

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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Shastri, A.S., Jagetia, A., Sehgal, A., Patel, M., Kulkarni, A.J. (2019). Expectation Algorithm (ExA): A Socio-inspired Optimization Methodology. 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_10

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