Journal ArticleDOI
A generic framework for constrained optimization using genetic algorithms
S. Venkatraman,Gary G. Yen +1 more
TLDR
This paper elaborate on how the constrained optimization problem requires a balance of exploration and exploitation under different problem scenarios and come to the conclusion that a nondominated ranking between the individuals will help the algorithm explore further, while the elitist scheme will facilitate in exploitation.Abstract:
In this paper, we propose a generic, two-phase framework for solving constrained optimization problems using genetic algorithms. In the first phase of the algorithm, the objective function is completely disregarded and the constrained optimization problem is treated as a constraint satisfaction problem. The genetic search is directed toward minimizing the constraint violation of the solutions and eventually finding a feasible solution. A linear rank-based approach is used to assign fitness values to the individuals. The solution with the least constraint violation is archived as the elite solution in the population. In the second phase, the simultaneous optimization of the objective function and the satisfaction of the constraints are treated as a biobjective optimization problem. We elaborate on how the constrained optimization problem requires a balance of exploration and exploitation under different problem scenarios and come to the conclusion that a nondominated ranking between the individuals will help the algorithm explore further, while the elitist scheme will facilitate in exploitation. We analyze the proposed algorithm under different problem scenarios using Test Case Generator-2 and demonstrate the proposed algorithm's capability to perform well independent of various problem characteristics. In addition, the proposed algorithm performs competitively with the state-of-the-art constraint optimization algorithms on 11 test cases which were widely studied benchmark functions in literature.read more
Citations
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Journal ArticleDOI
Constraint-Handling in Nature-Inspired Numerical Optimization: Past, Present and Future
TL;DR: An analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms and the most popular approaches are analyzed in more detail.
Journal ArticleDOI
Bat algorithm for constrained optimization tasks
TL;DR: A new metaheuristic optimization algorithm, called bat algorithm (BA), is used to solve constraint optimization tasks, and the optimal solutions obtained are found to be better than the best solutions provided by the existing methods.
Journal ArticleDOI
Differential evolution with dynamic stochastic selection for constrained optimization
Min Zhang,Wenjian Luo,Xufa Wang +2 more
TL;DR: The dynamic stochastic selection (DSS) is put forward within the framework of multimember differential evolution, and from the experimental results, DSS-MDE is effective for constrained optimization.
Journal ArticleDOI
Ensemble of Constraint Handling Techniques
TL;DR: Experimental results show that the performance of ECHT is better than each single constraint handling method used to form the ensemble with the respective EA, and competitive to the state-of-the-art algorithms.
Proceedings ArticleDOI
Constrained Optimization by the ε Constrained Differential Evolution with Gradient-Based Mutation and Feasible Elites
Tetsuyuki Takahama,Setsuko Sakai +1 more
TL;DR: The epsivDE is improved to solve problems with many equality constraints by introducing a gradient-based mutation that finds feasible point using the gradient of constraints at an infeasible point and to find feasible solutions faster by introducing elitism.
References
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Journal ArticleDOI
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Journal ArticleDOI
No free lunch theorems for optimization
TL;DR: A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving and a number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class.
Book
Practical Methods of Optimization
TL;DR: The aim of this book is to provide a Discussion of Constrained Optimization and its Applications to Linear Programming and Other Optimization Problems.