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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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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