Journal ArticleDOI
A simple multimembered evolution strategy to solve constrained optimization problems
TLDR
The proposed approach to solve global nonlinear optimization problems uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population to find the global optimum despite reaching reasonably fast the feasible region of the search space.Abstract:
This work presents a simple multimembered evolution strategy to solve global nonlinear optimization problems. The approach does not require the use of a penalty function. Instead, it uses a simple diversity mechanism based on allowing infeasible solutions to remain in the population. This technique helps the algorithm to find the global optimum despite reaching reasonably fast the feasible region of the search space. A simple feasibility-based comparison mechanism is used to guide the process toward the feasible region of the search space. Also, the initial stepsize of the evolution strategy is reduced in order to perform a finer search and a combined (discrete/intermediate) panmictic recombination technique improves its exploitation capabilities. The approach was tested with a well-known benchmark. The results obtained are very competitive when comparing the proposed approach against other state-of-the art techniques and its computational cost (measured by the number of fitness function evaluations) is lower than the cost required by the other techniques compared.read more
Citations
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Journal ArticleDOI
Biogeography-Based Optimization
TL;DR: This paper discusses natural biogeography and its mathematics, and then discusses how it can be used to solve optimization problems, and sees that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO).
Journal ArticleDOI
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems
TL;DR: The effectiveness of the TLBO method is compared with the other population-based optimization algorithms based on the best solution, average solution, convergence rate and computational effort and results show that TLBO is more effective and efficient than the other optimization methods.
Journal ArticleDOI
Harris hawks optimization: Algorithm and applications
Ali Asghar Heidari,Ali Asghar Heidari,Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah,Majdi Mafarja,Huiling Chen +6 more
TL;DR: The statistical results and comparisons show that the HHO algorithm provides very promising and occasionally competitive results compared to well-established metaheuristic techniques.
Journal ArticleDOI
Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems
TL;DR: A comparative study has been carried out to show the effectiveness of the WCA over other well-known optimizers in terms of computational effort and function value in this paper.
Journal ArticleDOI
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
TL;DR: A co-evolutionary particle swarm optimization approach (CPSO) for constrained optimization problems, where PSO is applied with two kinds of swarms for evolutionary exploration and exploitation in spaces of both solutions and penalty factors.
References
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Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Genetic algorithms in search, optimization and machine learning
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book
Evolutionary algorithms for solving multi-objective problems
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Introduction to Evolutionary Computing
Agoston E. Eiben,James C. Smith +1 more
TL;DR: In the second edition, the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations as discussed by the authors.