Abstract
Metaheuristic algorithms are important tools that in recent years have been used extensively in several fields. In engineering, there is a big amount of problems that can be solved from an optimization point of view. This paper is an introduction of how metaheuristics can be used to solve complex problems of engineering. Their use produces accurate results in problems that are computationally expensive. Experimental results support the performance obtained by the selected algorithms in such specific problems as digital filter design, image processing and solar cells design.
Highlights
IntroductionPractically everything can be optimized. Optimization can be included in different subfields, for example, quality control, design, surveillance, etc
In engineering, practically everything can be optimized
A second order system using a first order model has been selected as an example
Summary
Practically everything can be optimized. Optimization can be included in different subfields, for example, quality control, design, surveillance, etc. Such approaches use our scientific understanding of biological, natural or social systems, which at some level of abstraction can be represented as optimization processes, as inspiration These methods include the social behavior of bird flocking and fish schooling such as the Particle Swarm Optimization (PSO) algorithm [1], the cooperative behavior of bee colonies such as the Artificial Bee Colony (ABC) technique [2], the improvisation process that occurs when a musician searches for a better state of harmony such as the Harmony Search (HS) [3], the emulation of the bat behavior such as the Bat Algorithm (BA) method [4], the mating behavior of firefly insects such as the Firefly (FF) method [5], the social-spider behavior such as the Social Spider Optimization (SSO) [6], the simulation of the animal behavior in a group such as the Collective Animal Behavior [6], the emulation of immunological systems as the clonal selection algorithm (CSA) [7], the simulation of the electromagnetism phenomenon as the electromagnetism-Like algorithm [8], and the emulation of the differential and conventional evolution in species such as the Differential Evolution (DE) [9] and Genetic Algorithms (GA) [10], respectively.
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