MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover

Jiang, Qiaoyong; Wang, Lei; Hei, Xinhong; Yu, Guolin; Lin, Yanyan; Lu, Xiaofeng

doi:10.1016/j.knosys.2016.06.007

Cited by 29 publications

(2 citation statements)

References 34 publications

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“…So far, studies based on MOEA/D have been gradually received attention. For instance, it has combined with swarm intelligence [14], local search strategy [15], and adaptive mechanism [16].…”

Section: Definition 2 (Pareto Solution)mentioning

confidence: 99%

Adaptive Truncation technique for Constrained Multi-Objective Optimization

2019

KSII TIIS

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The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

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Section: Definition 2 (Pareto Solution)mentioning

confidence: 99%

Adaptive Truncation technique for Constrained Multi-Objective Optimization

2019

KSII TIIS

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“…The optimization of the decision variables are repeated until all N decision variable, x1, x2, ..., xN-1, and xN are optimized. With the use of the multistage search, the solution search space is significantly reduced so that the optimized solution obtained from the multistage search is better than an optimized solution searched from a full solution represented by N decision variables by normal GA.Table 1 -GA parameters settingsParametersSettings and valuesChromosome codingThe real-value chromosome with 250 decision variables.Number of decision variables in each solution search (NVE)GA with no MS: 250 GA with MS : 125, 25, 5 Fitness scalingLinear fitness scaling with a factor of 2.0 Crossover method SBX crossover[12],[13] with probability = 1.0 Mutation method Variable-wise polynomial mutation[14] with probability = 0Convergence of objective value of GA with no MS for both 2 cases (a) Case 1 a) Case 2…”

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confidence: 99%

Road Profile Identification by Genetic Algorithm withMultistage Search using Vibration Response of a QuarterVehicle Model

Jeenkour¹,

Boonlong

2019

IJIE

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Vehicle performance is affected by road profile since the road profile supplies vibration to the vehicle. The road profile can be identified by vibration response of the vehicle. By using the vibration response, road profile identification can be, in fact, formulated as a single-objective optimization in which an objective, a minimization criterion, is the numerical difference between vehicle vibration response of actual road profile and that of predicted road profile. This paper present multistage search in genetic algorithm (GA), an optimization algorithm, in the road profile identification. In the multistage search, a solution is divided into a number of parts and each part is consequently evolved as GA process separately from other parts. A quarter vehicle models with two test cases of double bump on road profiles are used. Simulation runs reveal that the multistage search can enhance performance of GA. In addition, the multistage search using the least number of decision variables in each solution part gives the best results of the optimized solutions.

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Multiobjective Evolutionary Optimization

Biswas

Suganthan

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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Many computational techniques have been known for years to solve multiobjective optimization problems (MOPs). However, the nature ofMOPs has been changing and many more large‐scale multimodalMOPs, computationally expensiveMOPs, dynamicMOPs, noisyMOPs, and so on are introduced in multiobjective optimization domain. The researchers are thus inspired to look beyond the conventional approaches and focus more on evolutionary optimization techniques. The developments in the field of evolutionary algorithm (EA) in last few decades makeEAan effective tool to apply to complexMOPs. This article provides an overview of multiobjective evolutionary algorithms (MOEAs), different frameworks ofMOEAs, and the application ofMOEAs to variousMOPs. Performance indicators forMOEAs and some visualization methods in many‐objective optimization problems are also briefly mentioned in this article.

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MOEA/D-ARA+SBX: A new multi-objective evolutionary algorithm based on decomposition with artificial raindrop algorithm and simulated binary crossover

Cited by 29 publications

References 34 publications

Adaptive Truncation technique for Constrained Multi-Objective Optimization

Adaptive Truncation technique for Constrained Multi-Objective Optimization

Road Profile Identification by Genetic Algorithm withMultistage Search using Vibration Response of a QuarterVehicle Model

Multiobjective Evolutionary Optimization

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