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Optimization Algorithms Surpassing Metaphor

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Computational Intelligence for Water and Environmental Sciences

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1043))

Abstract

Achieving novel systems with optimal performance has been converted into a critical preoccupation amongst engineers and scientists across a wide range of fields, whereby optimization has played a crucial role in current research. Indeed, optimizers find the collection of variables to reach the optimal amount of cost functions by considering the possible domains of the variables restricted. algorithms, of Meta‐heuristic and evolutionary, inspired by natural behaviour, mathematical foundations, and physics, are considered optimization techniques commonly hired to determine optimal solutions. Notwithstanding the traditional optimization methods, comprised of linear, nonlinear, integer, and dynamic programming, algorithms, of meta‐heuristic and evolutionary, provide reliable modeling results in various engineering subject matters like real‐world and complex obstacles. In addition, although the number of algorithms based on natural behaviours has increased, the majority of them deal with some ordeals such as being stuck in local optimal results. Hence, these ordeals pave the way for the advent of new mathematical, population-based techniques. In this chapter, three powerful metaphor-free, population-based optimization approaches are introduced, being categorized into gradient-based optimizer (GBO), Runge Kutta optimizer (RUN), and differential evolution (DE). Each technique is evaluated in terms of the basic concept; the algorithms are then described based on mathematical statements. In the last part, the pseudo-code of the methods and the guidelines for coding are presented. This chapter is intended for those who have an interest in engineering optimization.

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Author information

Authors and Affiliations

  1. Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran

    Arvin Samadi-Koucheksaraee & Iman Ahmadianfar

  2. Department of Chemical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Seyedehelham Shirvani-Hosseini

  3. School of Engineering, University of Guelph, Guelph, Ontario, N1G 2W1, Canada

    Bahram Gharabaghi

Authors
  1. Arvin Samadi-Koucheksaraee
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  2. Seyedehelham Shirvani-Hosseini
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  3. Iman Ahmadianfar
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  4. Bahram Gharabaghi
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Correspondence to Arvin Samadi-Koucheksaraee .

Editor information

Editors and Affiliations

  1. College of Agriculture & Natural Resources, University of Tehran, Karaj, Alborz, Iran

    Omid Bozorg-Haddad

  2. QUEX Joint-researcher, Centre for Water in the Minerals Industry, Sustainable Minerals Institute, The University of Queensland, Brisbane, QLD, Australia

    Babak Zolghadr-Asli

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Samadi-Koucheksaraee, A., Shirvani-Hosseini, S., Ahmadianfar, I., Gharabaghi, B. (2022). Optimization Algorithms Surpassing Metaphor. In: Bozorg-Haddad, O., Zolghadr-Asli, B. (eds) Computational Intelligence for Water and Environmental Sciences. Studies in Computational Intelligence, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-19-2519-1_1

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