Abstract
Optimization problems can be observed in many applications, ranging from engineering applications and decision-making to computer science and finance. Optimization can be described as a process for discovering optimal solution among all available solutions of the defined problem, considering complex and high-dimensional constraints in searching for the optimal solution. Among the numerous approaches for solving optimization issues, optimization algorithms are one of the most well-known methods. Such algorithms possess the ability and efficiency to solve optimization issues in different science and engineering disciplines; however, these algorithms face problems, such as slow convergence speed and falling into local optimum. Various methods can be applied to improve optimization algorithms’ performance. This chapter aims to introduce some of these methods and their theory, along with different combination methods to achieve better performance in finding the global solution.
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Yaghoubzadeh-Bavandpour, A., Bozorg-Haddad, O., Zolghadr-Asli, B., Gandomi, A.H. (2022). Improving Approaches for Meta-heuristic Algorithms: A Brief Overview. 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_2
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