Multi-objective chaos game optimization

Khodadadi, Nima; Abualigah, Laith; Al-Tashi, Qasem; Mirjalili, Seyedali

doi:10.1007/s00521-023-08432-0

Cited by 20 publications

(3 citation statements)

References 56 publications

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“…MOMVO, MOSMA, MOALO, MOMPA, and Multiobjective Arithmetic Optimization Algorithm (MOAOA) [18] use ND sorting and crowding distance mechanisms. Multiobjective grey Wolf optimizer (MOGWO) [19], Multi-objective Chaos Game Optimization [20], and multi-objective spotted hyena optimizer (MOSHO) [13] use grid, archive, and leader selection mechanisms. Multi-objective Coronavirus disease optimization algorithm [21] makes use of ND sorting, archive and leader selection methods.…”

Section: Related Workmentioning

confidence: 99%

Cheetah Optimizer for Multi-objective Optimization Problems

Sharma,

Kumar

2023

Preprint

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In this paper, a new algorithm named multi-objective cheetah optimizer is presented for solving multi-objective optimization problems. Cheetah optimizer is a new optimization algorithm, which has been proven to be more effective for solving large-scale and complex optimization problems. The proposed MOCO is developed from the single-objective cheetah optimizer by introducing the concepts of non-dominance sorting and archiving. Non-dominance sorting is used to get Pareto optimal solutions. An Archive is used for improving and maintaining their distribution. The experimental results show that the proposed algorithm performs better than the existing multi-objective algorithms in terms of fitness value. The Pareto-optimal fronts exhibit good convergence and coverage. The empirical comparison results of the proposed algorithm with existing multi-objective algorithms exhibit its competitiveness. Simulation studies were performed on well-known multi-objective benchmark functions and real-world engineering design optimization problems to verify the proposed MO algorithm and ensure its applicability in real-life scenarios. Comparative analysis is done for the proposed multi-objective cheetah optimizer and other multi-objective algorithms that have recently been proposed.

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Section: Related Workmentioning

confidence: 99%

Cheetah Optimizer for Multi-objective Optimization Problems

Sharma,

Kumar

2023

Preprint

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“…Ideal point methods were used to determine the weights of the objective function using pairwise comparison methods in order to find a compromise. Khodadadi et al in [2] completed a multi-objective chaos game optimization task with leader selection functionality applied to eight real-world multi-objective engineering design challenges.…”

Section: State Of Knowledgementioning

confidence: 99%

Comparison of Multi-Object Control Methods Using Multi-Objective Optimization

Lisowski

2023

Electronics

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The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of the greater effectiveness of bi- and tri-objective optimization compared to single-objective optimization, reflecting the compromises taken into account when choosing between objects and achieving a minimum risk of collision when passing them.

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“…Optimization problems can be found in almost any field of study [ 10 ]. Some of the most popular areas are medicine [ 11 ], engineering problems [ 12 , 13 , 14 ], image processing [ 15 ], feature selection [ 16 ], etc. [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications

et al. 2023

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Metamaterials have unique physical properties. They are made of several elements and are structured in repeating patterns at a smaller wavelength than the phenomena they affect. Metamaterials’ exact structure, geometry, size, orientation, and arrangement allow them to manipulate electromagnetic waves by blocking, absorbing, amplifying, or bending them to achieve benefits not possible with ordinary materials. Microwave invisibility cloaks, invisible submarines, revolutionary electronics, microwave components, filters, and antennas with a negative refractive index utilize metamaterials. This paper proposed an improved dipper throated-based ant colony optimization (DTACO) algorithm for forecasting the bandwidth of the metamaterial antenna. The first scenario in the tests covered the feature selection capabilities of the proposed binary DTACO algorithm for the dataset that was being evaluated, and the second scenario illustrated the algorithm’s regression skills. Both scenarios are part of the studies. The state-of-the-art algorithms of DTO, ACO, particle swarm optimization (PSO), grey wolf optimizer (GWO), and whale optimization (WOA) were explored and compared to the DTACO algorithm. The basic multilayer perceptron (MLP) regressor model, the support vector regression (SVR) model, and the random forest (RF) regressor model were contrasted with the optimal ensemble DTACO-based model that was proposed. In order to assess the consistency of the DTACO-based model that was developed, the statistical research made use of Wilcoxon’s rank-sum and ANOVA tests.

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Multi-objective chaos game optimization

Cited by 20 publications

References 56 publications

Cheetah Optimizer for Multi-objective Optimization Problems

Cheetah Optimizer for Multi-objective Optimization Problems

Comparison of Multi-Object Control Methods Using Multi-Objective Optimization

Improved Dipper-Throated Optimization for Forecasting Metamaterial Design Bandwidth for Engineering Applications

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