October 2017
Intermediate to advanced
304 pages
8h 3m
English
book
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
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22 Comprehensive Evolutionary Algorithm
Summary
This chapter introduces a new meta‐heuristic optimization algorithm called the comprehensive evolutionary algorithm (CEA). This algorithm combines and takes advantages of some aspects of various algorithms, especially the genetic algorithm (GA) and the honey‐bee mating optimization (HBMO) algorithm. The following sections describe the fundamentals of the CEA and its algorithmic details. The chapter closes with a pseudocode of the CEA.
22.1 Introduction
The comprehensive evolutionary algorithm (CEA) is an optimization algorithm of recent vintage that combines features of the genetic algorithm (GA) and the honey‐bee mating optimization (HBMO) algorithm. The CEA can solve single and multi‐objective problems. This algorithm optimizes the defined objective function of an optimization problem based on three processes: (1) selection, (2) production, and (3) replacement. In addition, the CEA is able to explicitly perform sensitivity analysis of some of its parameters based on the problem conditions. In general, the CEA has better convergence performance and speed to the near‐optimal solution, on the optimality of final solution, and on the run time period.
The GA was developed by Holland (1975), inspired by evolutionary process that are emulated mathematically in the GA. Numerous researches have been carried out to improve, extend, and apply the GA to a wide variety of optimization problems (Dimou and Koumousis, 2003; Hormwichian ...
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