Guided Manta Ray foraging optimization using epsilon dominance for multi-objective optimization in engineering design

Zouache, Djaafar; Abdelaziz, Fouad Ben

doi:10.1016/j.eswa.2021.116126

Cited by 22 publications

(13 citation statements)

References 37 publications

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“…Meanwhile, a crowding distance and ε-dominance are utilized to provide a good compromise between diversity and convergence of the obtained potential Pareto set. The experimental results demonstrate that the MOMRFO algorithm outperforms and can provide better convergence and diversity of solutions [10].…”

Section: Introductionmentioning

confidence: 92%

“…Allocate the crowding distance for each individual using normalization due to the projection of adjacent individuals at the same level. The crowding distance could be calculated using Equation (10).…”

Section: Imocs Implementation Stepsmentioning

confidence: 99%

“…In the Equation (10), f m (x i+1 ) and f m (x i−1 ) respectively denote the m th objective function value of the i + 1 and i − 1 individuals. f max m and f min m denote the maximum and minimum function values of the m th objective function.…”

Section: Imocs Implementation Stepsmentioning

confidence: 99%

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An Improved Multi-Objective Cuckoo Search Approach by Exploring the Balance between Development and Exploration

Zhou

Zhang

et al. 2022

Electronics

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In recent years, multi-objective cuckoo search (MOCS) has been widely used to settle the multi-objective (MOP) optimization issue. However, some drawbacks still exist that hinder the further development of the MOCS, such as lower convergence accuracy and weaker efficiency. An improved MOCS (IMOCS) is proposed in this manuscript by investigating the balance between development and exploration to obtain more accurate solutions while solving the MOP. The main contributions of the IMOCS could be separated into two aspects. Firstly, a dynamic adjustment is utilized to enhance the efficiency of searching non-dominated solutions in different periods utilizing the Levy flight. Secondly, a reconstructed local dynamic search mechanism and disturbance strategy are employed to strengthen the accuracy while searching non-dominated solutions and to prevent local stagnation when solving complex problems. Two experiments are implemented from different aspects to verify the performance of the IMOCS. Firstly, seven different multi-objective problems are optimized using three typical approaches, and some statistical methods are used to analyze the experimental results. Secondly, the IMOCS is applied to the obstacle avoidance problem of multiple unmanned aerial vehicles (UAVs), for seeking a safe route through optimizing the coordinated formation control of UAVs to ensure the horizontal airspeed, yaw angle, altitude, and altitude rate are converged to the expected level within a given time. The experimental results illustrate that the IMOCS can make the multiple UAVs converge in a shorter time than other comparison algorithms. The above two experimental results indicate that the proposed IMOCS is superior to other algorithms in convergence and diversity.

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Section: Introductionmentioning

confidence: 92%

Section: Imocs Implementation Stepsmentioning

confidence: 99%

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An Improved Multi-Objective Cuckoo Search Approach by Exploring the Balance between Development and Exploration

Zhou

Zhang

et al. 2022

Electronics

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show abstract

“…The proposed algorithm uses an external archive population to store non-dominant solutions based on the Epsilon dominance found during the search best solutions [31] . Initially, this external archive initially contains all non-dominated solutions from the initial parent population

.…”

Section: Proposed Algorithm: G-moea/d-samentioning

confidence: 99%

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19

Hemici

Zouache

Brahmi

et al. 2023

Applied Soft Computing

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“…We aimed to gauge their capabilities in swiftly converging to the true Pareto optimal front and the distribution of the obtained non-dominated solutions. Upon assessing their convergence and coverage using MO metrics and the produced Pareto optimal fronts on benchmark suites (ZDT [ 54 ], DTLZ [ 54 ], Constraint [ 68 , 69 ] and engineering design problems [ 55 , 56 ]), we discerned that these algorithms still exhibited shortcomings in convergence and coverage using metrics like generational distance (GD) [ 70 ], inverse generational distance (IGD) [ 71 ], hypervolume [ 72 ], Spacing [ 73 ], Spread [ 72 ] and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Kalita,

Naga Ramesh,

Čep

et al. 2024

Heliyon

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Guided Manta Ray foraging optimization using epsilon dominance for multi-objective optimization in engineering design

Cited by 22 publications

References 37 publications

An Improved Multi-Objective Cuckoo Search Approach by Exploring the Balance between Development and Exploration

An Improved Multi-Objective Cuckoo Search Approach by Exploring the Balance between Development and Exploration

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

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