Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

Rizk‐Allah, Rizk M.; Hassanien, Aboul Ella; Słowik, Adam

doi:10.1007/s00521-020-04779-w

Cited by 48 publications

(11 citation statements)

References 35 publications

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“…The objectives of the design are to minimize the cost of fabrication and to minimize the deflection. This problem is well-studied in both mono- [55][56][57] and multi-objective [52,54,[58][59][60] literature. In the optimization stage, the NSGA-II, GWASF-GA and MOEA/D algorithms and the g-NSGA-II and WASF-GA algorithms (that include DM's partial-preferences as a reference point) were implemented with binary coding.…”

Section: Resultsmentioning

confidence: 99%

“…Initially, the modified NSGA-II is employed for multi-objective optimization of the sub-frame, and then, by means of entropy weight theory and TOPSIS method, all the obtained solutions are ranked from the best to the worst in order to determine the best compromise solution. In [52], a decision-making tool based on multi-objective optimization technique MOORA is proposed. MOORA helps the designer for extracting the operating point as the best compromise solution to execute the candidate engineering design.…”

Section: Introductionmentioning

confidence: 99%

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TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

et al. 2020

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A common technique used to solve multi-objective optimization problems consists of first generating the set of all Pareto-optimal solutions and then ranking and/or choosing the most interesting solution for a human decision maker (DM). Sometimes this technique is referred to as generate first–choose later. In this context, this paper proposes a two-stage methodology: a first stage using a multi-objective evolutionary algorithm (MOEA) to generate an approximate Pareto-optimal front of non-dominated solutions and a second stage, which uses the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) devoted to rank the potential solutions to be proposed to the DM. The novelty of this paper lies in the fact that it is not necessary to know the ideal and nadir solutions of the problem in the TOPSIS method in order to determine the ranking of solutions. To show the utility of the proposed methodology, several original experiments and comparisons between different recognized MOEAs were carried out on a welded beam engineering design benchmark problem. The problem was solved with two and three objectives and it is characterized by a lack of knowledge about ideal and nadir values.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

et al. 2020

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“…First, two individuals are randomly chosen to undergo the crossover stage and then orthogonal array is presented to obtain nine individuals. Then individuals are used in the opposition stage to improve the diversity of solutions [34] Hybrid sine-cosine algorithm with multiorthogonal search strategy (MOSS)…”

Section: Strategy Of Modification/hybridization Year Referencesmentioning

confidence: 99%

Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

Dey

Bhattacharyya

Raj

et al. 2020

Journal of Electrical Systems and Inf Technol

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Economic emission dispatch (EED) of a three-unit stand-alone microgrid system supported by a wind farm is percolated in this paper. The adverse effects of stochastic and uncertainty nature of wind energy in raising the generation cost of the microgrid system are studied in this article. Unit commitment (UC) of the generating units is taken into account which helps in reducing the generation cost and provides relaxation time to the generation units. Three cases are contemplated for the study. For the first two cases, the generation cost of the test system was minimized without and with the involvement of wind power, respectively. The third case considered the involvement of wind power along with the UC of the conventional generation units. A novel hybrid of recently developed superior optimization algorithms, viz. grey wolf optimizer (GWO), sine–cosine algorithm (SCA) and crow search algorithm (CSA), is implemented to perform EED, and the results are compared with basic GWO and other hybrid algorithms. Results are then analysed to compare and contrast among these cases and justify the reliable and profitable one. Statistical analysis claims the superiority of the proposed hybrid MGWOSCACSA over other hybrids and GWO.

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“…Furthermore, researchers in the field of artificial intelligence have proposed a range of multi-objective developments such as the Multiple Objective Particle Swarm Optimization (MOPSO) by Coello and Lechuga [31], Multi-Objective Evolutionary Algorithm (MOEA) by Zhang and Li [32], Multi-Objective Ant Colony Optimization (MOACO) by Alaya et al [33], and Multi-Objective Simulated Annealing (MOSA) by Smith et al [34]. Besides, the multi-objective versions of some other recently proposed metaheuristic algorithms have also been proposed, such as the multi-objective seagull optimization algorithm [35], multiobjective forest optimization algorithm [36], multi-objective whale optimization algorithm with differential evolution [37], multi-objective crow search algorithm [38], and Multi-objective Slap Swarm Algorithm (MSSA) [39].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Crystal Structure Algorithm (MOCryStAl): Introduction and Performance Evaluation

et al. 2021

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In many optimization problems, the main goal is to improve a single performance index in which a minimum or maximum value of this index fully reflects the quality of the response obtained from a system. However, in some cases, it is impossible to rely solely on a single index, so a multiobjective optimization problem with multiple performance indicators is considered where the values of all of them should be optimized simultaneously. The mentioned process requires a multi-objective optimization algorithm that can deal with the complexity of problems with simultaneous indexes. This paper presents the multi-objective version of a recently proposed metaheuristic algorithm called Crystal Structure Algorithm (CryStAl) which was inspired by the principles underlying the formation crystal structures. For the performance evaluation of this algorithm which is called MOCryStAl, the benchmark problems of the Completions on Evolutionary Computation (CEC) on multi-objective optimization, called CEC-09, are utilized. Some real-world engineering design problems are used to evaluate the efficiency of the proposed approach. The results demonstrate that the proposed methods can provide excellent results in dealing with the considered multi-objective problems.INDEX TERMS Multi-objective optimization, metaheuristic, crystal structure algorithm (CryStAl), completions on evolutionary computation (CEC), real-world engineering design problem.

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Multi-objective orthogonal opposition-based crow search algorithm for large-scale multi-objective optimization

Cited by 48 publications

References 35 publications

TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem

Economic emission dispatch on unit commitment-based microgrid system considering wind and load uncertainty using hybrid MGWOSCACSA

Multi-Objective Crystal Structure Algorithm (MOCryStAl): Introduction and Performance Evaluation

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