Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

Lagaros, Nikos D.; Kournoutos, Makis; Kallioras, Nikos Ath.; Nordas, Alexandros N.

doi:10.1007/s11081-022-09782-9

Cited by 11 publications

(4 citation statements)

References 64 publications

(88 reference statements)

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“…For three CE plans, both DE and PSO methods are used to compute the optimal values of the decision variables (𝑇 0 , 𝜉 1 , 𝜉 2 , 𝐺, 𝑊 𝑟 ) and the accompanying optimal profit. To handle constraints in our highly non-linear optimization model, we use penalty method for constraint handling in metaheuristics (Lagaros et al, 2023).…”

Section: Optimization Via Metaheuristic Approachesmentioning

confidence: 99%

“…Akhtar et al (2023) evaluated the total profit using a unique hybrid approach based on DE and social group optimization methodologies. Lagaros et al (2023) studied the use of specialized metaheuristic approaches that simplify the handling of performance and bound constraints of optimization problems. In our model, we are concerned with a constrained optimization model which is difficult to solve by using analytic approaches.…”

Section: Introductionmentioning

confidence: 99%

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ANFIS and metaheuristic optimization for green supply chain with inspection and rework

Sharma,

Jain,

Sharma

2024

Preprint

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The focus of present article is to investigate a supply chain inventory model along with inspection and stock dependent demand with use of green technology to reduce carbon emissions. Products that are decaying, or those that change over time, have a high sensitivity to the environment in terms of temperature, carbon emission, humidity, waste disposal, etc. This study develops a profit maximization model in the presence of deterioration, preservation, imperfect production, inspection error, rework, stock and price-dependent demand. The three carbon emission strategies are proposed to reduce the expenses in different carbon emissions scenarios. The suggested approach may be used to determine the optimal production period, preservation investment, and level of green investment. The solution of the non-linear constraint optimization is provided by using a penalty method in metaheuristic approaches. In order to conduct a sensitivity analysis for the essential model parameters, a numerical example is presented. The soft computing results produced by DE and PSO are compared with the results obtained by Adaptive Neuro-Fuzzy Inference System (ANFIS) technique.

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Section: Optimization Via Metaheuristic Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ANFIS and metaheuristic optimization for green supply chain with inspection and rework

Sharma,

Jain,

Sharma

2024

Preprint

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“…The objective function f → x is the fitness function in the engineering optimization problem [29], where → x is the search space and distinct variables are the dimensions. The objective function, constraints, and variable interval are all subject to equal or unequal restrictions.…”

Section: Practical Engineering Applicationmentioning

confidence: 99%

An Improved Golden Jackal Optimization Algorithm Based on Mixed Strategies

Li,

Yu,

Wang

et al. 2024

Mathematics

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In an effort to overcome the problems with typical optimization algorithms’ slow convergence and tendency to settle on a local optimal solution, an improved golden jackal optimization technique is proposed. Initially, the development mechanism is enhanced to update the prey’s location, addressing the limitation of just relying on local search in the later stages of the algorithm. This ensures a more balanced approach to both algorithmic development and exploration. Furthermore, incorporating the instinct of evading natural predators enhances both the effectiveness and precision of the optimization process. Then, cross-mutation enhances population variety and facilitates escaping from local optima. Finally, the crossbar strategy is implemented to change both the individual and global optimal solutions of the population. This technique aims to decrease blind spots, enhance population variety, improve solution accuracy, and accelerate convergence speed. A total of 20 benchmark functions are employed for the purpose of comparing different techniques. The enhanced algorithm’s performance is evaluated using the CEC2017 test function, and the results are assessed using the rank-sum test. Ultimately, three conventional practical engineering simulation experiments are conducted to evaluate the suitability of IWKGJO for engineering issues. The results obtained demonstrate the beneficial effects of the altered methodology and illustrate that the expanded golden jackal optimization algorithm has superior convergence accuracy and a faster convergence rate.

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“…The field of optimization has grown rapidly during the past few decades, and many new theoretical, algorithmic, and computational contributions have been proposed to solve various problems [18][19][20]. Recent developments in the field of optimization methods can mainly be divided into deterministic and heuristic approaches.…”

Section: -Literature Reviewmentioning

confidence: 99%

A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

Pietropolli¹,

Menara²,

Castelli³

2023

Emerg Sci J

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Some optimization problems are difficult to solve due to a considerable number of local optima, which may result in premature convergence of the optimization process. To address this problem, we propose a novel heuristic method for constructing a smooth surrogate model of the original function. The surrogate function is easier to optimize but maintains a fundamental property of the original rugged fitness landscape: the location of the global optimum. To create such a surrogate model, we consider a linear genetic programming approach coupled with a self-tuning fitness function. More specifically, to evaluate the fitness of the produced surrogate functions, we employ Fuzzy Self-Tuning Particle Swarm Optimization, a setting-free version of particle swarm optimization. To assess the performance of the proposed method, we considered a set of benchmark functions characterized by high noise and ruggedness. Moreover, the method is evaluated over different problems’ dimensionalities. The proposed approach reveals its suitability for performing the proposed task. In particular, experimental results confirm its capability to find the global argminimum for all the considered benchmark problems and all the domain dimensions taken into account, thus providing an innovative and promising strategy for dealing with challenging optimization problems. Doi: 10.28991/ESJ-2023-07-04-01 Full Text: PDF

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Constraint handling techniques for metaheuristics: a state-of-the-art review and new variants

Cited by 11 publications

References 64 publications

ANFIS and metaheuristic optimization for green supply chain with inspection and rework

ANFIS and metaheuristic optimization for green supply chain with inspection and rework

An Improved Golden Jackal Optimization Algorithm Based on Mixed Strategies

A Genetic Programming Based Heuristic to Simplify Rugged Landscapes Exploration

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