A rough penalty genetic algorithm for constrained optimization

Lin, Chih-Hao

doi:10.1016/j.ins.2013.04.001

Cited by 68 publications

(24 citation statements)

References 28 publications

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“…Even though the penalty function is very simple and popular, it is not trivial to define a set of appropriate penalty factors. To circumvent this problem, researchers have proposed many variants of penalty function method to deal with penalty factor values, such as dynamic method (Paszkowicz, 2009), adaptive method (Tessema & Yen, 2009), co-evolved method (Coello Coello, 2000), fuzzy method (Wu, Yu, & Liu, 2001), and some other methods (Lin, 2013). Among these methods, the adaptive penalty function is one of the most competitive approaches.…”

Section: ) Penalty Functionmentioning

confidence: 99%

“…In this section, we will compare BSA-SAe with some classic and latest constrained optimization algorithms, which include stochastic ranking (SR) (Runarsson & Yao, 2000), nonlinear simplex method with mutations using the a constrained method (a Simplex) , agent based memetic algorithm (AMA) (Ullah, Sarker, & Cornforth, 2007), modified artificial bee colony algorithm (MABC) (Karaboga & Akay, 2011), and penalty genetic algorithm based on rough set theory (RPGA) (Lin, 2013). The largest number of function evaluation on the 13 benchmark functions is set as 350,000 times, and each algorithm runs 30 times on each function independently.…”

Section: Comparison Of Bsa-sae and Other Constrained Optimization Algmentioning

confidence: 99%

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Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

Zhang

Lin

Gao

et al. 2015

Expert Systems with Applications

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Section: ) Penalty Functionmentioning

confidence: 99%

Section: Comparison Of Bsa-sae and Other Constrained Optimization Algmentioning

confidence: 99%

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

Zhang

Lin

Gao

et al. 2015

Expert Systems with Applications

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“…In addition, a cluster-replacement-based feasibility rule was developed to alleviate the greediness of the feasibility rule. To effectively handle constraints, genetic algorithm was hybridized with the rough set theory and the penalty function was used as constraint handling (Lin, 2013). Moreover, many bioinspired algorithms were applied to constrained optimization problem such as bacterial-inspired algorithm (Niu et al, 2015), elephant herding optimization (Ivana Strumberger et al, 2018), particle swarm optimization (Garg, 2016), grey wolf optimization algorithm (Kohli and Arora, 2017) etc.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach Based on Elephant Herding Optimization for Constrained Optimization Problems

Haklı¹

2019

Scitech

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Many real-world problems can be formulated as an optimization problem and they have some constraints generally. To overcome these constraints, bio-inspired algorithms are adapted to constrained optimization using constraint handling methods and some modifications. In this study, a new approach is developed to solve constrained optimization problems with elephant herding optimization algorithm which is a newly-emerging optimization technique. Besides the basic EHO, two EHO variants (EHO-NoB and GL-EHO) are adapted to constrained optimization with this approach. The well-known thirteen constrained benchmark functions are used to analysis the performances of algorithms. Experimental results show that the GL-EHO has a better performance than the basic EHO and other algorithms. In addition, the results of GL-EHO are comparable level with the result of another EHO variant in the literature.

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“…Some of the most popular EAs suggested in the literature are Genetic Algorithm (GA) [1], Particle Swarm Optimization (PSO) [2] , Simulated Annealing algorithm (SA) [3], Cultural Evolutionary algorithm [4], Modified Differential Evolution (COMDE) [5], Rough Penalty Genetic Algorithm (RPGA) [6], Modified Artificial Bee Colony algorithm (MABC) [7] etc. However, most of these algorithms are either complicated or suffer with computational burdensome to handle equality constraints.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Solving Engineering Optimization Problems Involving Equality Constraints

Das¹

2015

IJMMM

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Abstract-In general, the optimization problems arise in real life situations are constrained in nature. Handling such problems becomes more difficult when an optimization problem involves some equality constraints. The computation complexity increases exponentially if the constraints are non-linear and non-convex. In order to solve such problems, a large number of evolutionary approaches have been suggested in the literature. In such approaches, penalty function method and boundary simulation method are more popular to solve equality and inequality constraints problems. In this paper, an efficient and novel approach namely 'Drosophila Food-Search Constrained Optimization Algorithm (DFCOA)' has been proposed that helps for the robust generation of feasible regions during simulation. The proposed algorithm is initially tested on ten typical constrained benchmark problems with different tastes. Further two real life engineering problems have also been solved. The simulation results confirm that the proposed algorithm works better than some of the state-of-the-art algorithms.

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A rough penalty genetic algorithm for constrained optimization

Cited by 68 publications

References 28 publications

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

Backtracking Search Algorithm with three constraint handling methods for constrained optimization problems

A Novel Approach Based on Elephant Herding Optimization for Constrained Optimization Problems

A Novel Approach for Solving Engineering Optimization Problems Involving Equality Constraints

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