Metaheuristic optimization techniques

TL;DR: In this article , the authors provide the background, working principles, flowcharts, and pseudocodes of several popular and widely adopted metaheuristic algorithms for solving several optimization problems.

Abstract: Most of the real-world optimization problems involve various complexities, including nonlinearity, nonconvexity, discontinuity, and mixed variable types. In such cases, the classical algorithms are found to be either impractical or ineffective. In response, the search and optimization algorithms developed using heuristics offer a reasonable alternative. They are usually stochastic in nature and mimic the natural, physical, or biological principles. However, the metaheuristic techniques do not claim to achieve the exact global optimal solutions, but they always provide the near-optimal solution in a competitive time. Therefore, their practical appeal and ease of implementation, and the devise of high-speed computational devices made them popular in many application domains. This chapter provides the backgrounds, working principles, flowcharts, and pseudocodes of several popular and widely adopted metaheuristic algorithms. The discussion of the chapter shall assist the readers in understanding the fundamental features and differences of the explored algorithms that eventually guide them to choose the most appropriate algorithms for their problems. Moreover, many of the discussed algorithms are employed in solving several optimization problems throughout this book.