Multi-objective optimumA design of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models

Nikoo, Mohammad Reza; Varjavand, Iman; Kerachian, Reza; Pirooz, Moharram Dolatshahi; Karimi, Akbar

doi:10.1016/j.apor.2013.12.001

Cited by 35 publications

(10 citation statements)

References 13 publications

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“…, , , X X X X X  to minimize 12 ( , ) ff (7) so that Eq. (8) minimizes its cross-section area (the amount of used material):…”

Section: Find Vectormentioning

confidence: 99%

“…Multi-objective evolutionary sorting algorithms have been used in finding solutions of many multi-objective problems in the real world. They have been also widely used in optimizing engineering systems [8], [9] , [10], [6], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21]. On the other hand, the development of multiobjective optimization algorithms used in many scientific fields is unequivocal.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the development of multiobjective optimization algorithms used in many scientific fields is unequivocal. On the other hand, solving the disadvantage of multi-objective sorting algorithms (the NSGAII is the most well-known algorithm) leads us into to optimize many scientific problems [22], [23], [24], [25]. The algorithm proposed in this paper, the NAVNSGA algorithm tries to improve the disadvantage of NSGAI and NSGAII algorithms combinatorial to achieve balanced solutions.…”

Section: Introductionmentioning

confidence: 99%

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A New Multi-objective Sorting Algorithm And Its Combination With Game Theory For Optimizing I-beam Engineering System

Navidi¹,

Khanesar²,

Falahiazar³

2015

J. Math. Computer Sci.

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This paper proposed a new average non-dominated sorting genetic algorithm (NAVNSGA). This idea is inspired from the combination of non-elitist multi-objective evolutionary algorithms, elitist multiobjective evolutionary algorithms, and statistical calculations. The proposed NAVNSGA is improved the disadvantages of the Elitist multi-objective algorithms and Non-elitist multi-objective algorithms as possible. The NAVNSGA is compared with useful algorithms such as the non-elitist sorting genetic algorithm (NSGAI) and non-elitist sorting genetic algorithm (NSGAII) and the results obtained are showed the superiority of the proposed algorithm. Additionally, the NAVNSGA algorithm is combined with the concepts of the Game theory to propose a hybrid algorithm for determining Nash equilibrium in the game theory. The combination of the NAVNSGA algorithm with the game theory previously is used for improving engineering systems, such as I-beam designing. The results obtained are showed the advantage of the proposed algorithm with those reported in the literature.

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“…, , , X X X X X  to minimize 12 ( , ) ff (7) so that Eq. (8) minimizes its cross-section area (the amount of used material):…”

Section: Find Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Multi-objective Sorting Algorithm And Its Combination With Game Theory For Optimizing I-beam Engineering System

Navidi¹,

Khanesar²,

Falahiazar³

2015

J. Math. Computer Sci.

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“…Non-Dominant Sorting Genetic Algorithm (NSGA-II) was firstly proposed by Deb et al [33] to solve multi-objective optimization problem and has been used by many researchers so far [23,32,34,35]. In this type of problems, there is no single optimal solution since there could be various conflicts between objectives.…”

Section: Nsga-ii Multi-objective Optimization Modelmentioning

confidence: 99%

“…One of the main branches of this theory consists of the cooperative game methods [26][27][28][29].The studies which consist of a multi-objective optimization problem may have more than one solution, depending on the applied solution method [30][31]. In case, to select the best-obtained solution, Nikoo et al [32] applied Unanimity Fallback Bargaining (UFB) method to find the best agreed-upon design point on the trade-off curve.…”

Section: Introductionmentioning

confidence: 99%

A Game Theory Approach for Conjunctive Use Optimization Model Based on Virtual Water Concept

et al. 2018

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In this study to allocate the agricultural and environmental water, considering virtual water concept, a multi-objective optimization model based on NSGA-II is developed. The objectives consist of equity maximization, agricultural benefit maximization for each region, maximization of green water utilization and finally minimization of environmental shortage. Then a cooperative game (Grand Coalition) model is presented by forming all possible coalitions. By the game model including Nucleolus, Proportional Nucleolus, Normal Nucleolus and Shapley methods, the benefit is reallocated based on all Pareto optimal solutions obtained from multi-objective optimization model. Then using two famous fallback bargaining methods, Unanimity and q-Approval, preferable alternative (solution) for each of the cooperative games is determined. Finally, based on the obtained benefit for each selected alternatives, the two most beneficial alternatives are chosen. The proposed methodology applied for water allocation of Minoo-Dasht, Azad-Shahr and Gonbad-Kavoos cities in Golestan province, Iran for a 3-year period as a case study. Also, eight crops including Wheat, Alfalfa, Barley, Bean, Rice, Corn, Soya, and Cotton are selected based on local experts' recommendations. The models' results indicated no significant difference between the grand coalition model and the multi-objective optimization model in terms of the average cultivation area (a relative change of 2.1%), while lower agricultural water allocation occurred for the grand coalition model (about 10.35 percent average) compared with the multi-objective optimization model. It is also observed that more agricultural benefit gained by the grand coalition model (32 percent average). Finally, it is found that Wheat and Corn hold the most rates of import and export, respectively, and Rice was the crop which has the least shortage of production to supply food demand.

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Multi-Objective Optimization Application in Water and Environmental Sciences

Yaghoubzadeh-Bavandpour,

Bozorg-Haddad,

Zolghadr-Asli

et al. 2022

Computational Intelligence for Water and Environmental Sciences

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Multi-objective optimumA design of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models

Cited by 35 publications

References 13 publications

A New Multi-objective Sorting Algorithm And Its Combination With Game Theory For Optimizing I-beam Engineering System

A New Multi-objective Sorting Algorithm And Its Combination With Game Theory For Optimizing I-beam Engineering System

A Game Theory Approach for Conjunctive Use Optimization Model Based on Virtual Water Concept

Multi-Objective Optimization Application in Water and Environmental Sciences

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