A new real-coded genetic algorithm for implicit constrained black-box function optimization

Uemura, Kazunori; Nakashima, Naotoshi; Nagata, Yuichi; Ono, Isao

doi:10.1109/cec.2013.6557920

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Raghavan et al 13 developed an implicit constraint handling technique for optimization based on the proper orthogonal decomposition (POD) of shapes, further producing a bi-level reparameterization approach for structural geometries. Uemura et al 14 proposed a real-coded genetic algorithm for implicitly constrained black-box optimization, in which the method employs the weighted mean of the best individuals in a population to find the optimal solution.…”

Section: Openmentioning

confidence: 99%

Efficient implicit constraint handling approaches for constrained optimization problems

Rahimi,

Gandomi,

Nikoo

et al. 2024

Sci Rep

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Many real-world optimization problems, particularly engineering ones, involve constraints that make finding a feasible solution challenging. Numerous researchers have investigated this challenge for constrained single- and multi-objective optimization problems. In particular, this work extends the boundary update (BU) method proposed by Gandomi and Deb (Comput. Methods Appl. Mech. Eng. 363:112917, 2020) for the constrained optimization problem. BU is an implicit constraint handling technique that aims to cut the infeasible search space over iterations to find the feasible region faster. In doing so, the search space is twisted, which can make the optimization problem more challenging. In response, two switching mechanisms are implemented that transform the landscape along with the variables to the original problem when the feasible region is found. To achieve this objective, two thresholds, representing distinct switching methods, are taken into account. In the first approach, the optimization process transitions to a state without utilizing the BU approach when constraint violations reach zero. In the second method, the optimization process shifts to a BU method-free optimization phase when there is no further change observed in the objective space. To validate, benchmarks and engineering problems are considered to be solved with well-known evolutionary single- and multi-objective optimization algorithms. Herein, the proposed method is benchmarked using with and without BU approaches over the whole search process. The results show that the proposed method can significantly boost the solutions in both convergence speed and finding better solutions for constrained optimization problems.

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Section: Openmentioning

confidence: 99%

Efficient implicit constraint handling approaches for constrained optimization problems

Rahimi,

Gandomi,

Nikoo

et al. 2024

Sci Rep

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“…In this paper, we focus on unconstrained BBFO problems and implicitly constrained BBFO ones [10], [13], [14].…”

Section: Problem Settingmentioning

confidence: 99%

Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure

Nomura¹,

Ono²

2021

Preprint

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In this paper, we propose a new natural evolution strategy for unconstrained black-box function optimization (BBFO) problems and implicitly constrained BBFO problems. BBFO problems are known to be difficult because explicit representations of objective functions are not available. Implicit constraints make the problems more difficult because whether or not a solution is feasible is revealed when the solution is evaluated with the objective function. DX-NES-IC is one of the promising methods for implicitly constrained BBFO problems. DX-NES-IC has shown better performance than conventional methods on implicitly constrained benchmark problems. However, DX-NES-IC has a problem in that the moving speed of the probability distribution is slow on ridge structure. To address the problem, we propose the Fast Moving Natural Evolution Strategy (FM-NES) that accelerates the movement of the probability distribution on ridge structure by introducing the rank-one update into DX-NES-IC. The rank-one update is utilized in CMA-ES. Since naively introducing the rank-one update makes the search performance deteriorate on implicitly constrained problems, we propose a condition of performing the rank-one update. We also propose to reset the shape of the probability distribution when an infeasible solution is sampled at the first time. In numerical experiments using unconstrained and implicitly constrained benchmark problems, FM-NES showed better performance than DX-NES-IC on problems with ridge structure and almost the same performance as DX-NES-IC on the others. Furthermore, FM-NES outperformed xNES, CMA-ES, xNES with the resampling technique, and CMA-ES with the resampling technique.

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“…The GA is one of the meta-heuristic techniques that depends on the natural selection and genetics. GA is used to globally generate the best solutions for optimization problems [29]. GAs perform parallel search within a population of points.…”

Section: System Constraintsmentioning

confidence: 99%

Optimal DG Deployment Based on Technical and Economic Considerations with Daily Load Variation

el‐Ghany

Azmy

Saad³

2019

JERA

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The concept of distributed generation (DG) has been developed as a hopeful solution to meet the demand increase in distribution systems. The optimal DG deployment in the distribution system can improve the technical benefits. This study presents a framework to find the optimum location and size for different types of DG units to enhance the voltage stability index (VSI), improve the voltage regulation and reduce the daily energy losses. The emission effect and cost function (environment and economic benefits) are augmented in the multi-objective function. Combinations of DG types are introduced to obtain the best benefits from each one. The daily load variations and dynamic output of each DG type are considered in the problem formulation. The genetic algorithm (GA) technique is used to find the optimal allocation for different cases. The typical IEEE 33-bus system is utilized to evaluate the proposed framework.

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A new real-coded genetic algorithm for implicit constrained black-box function optimization

Cited by 9 publications

References 14 publications

Efficient implicit constraint handling approaches for constrained optimization problems

Efficient implicit constraint handling approaches for constrained optimization problems

Natural Evolution Strategy for Unconstrained and Implicitly Constrained Problems with Ridge Structure

Optimal DG Deployment Based on Technical and Economic Considerations with Daily Load Variation

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