A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D

Jan, Muhammad Asif; Khanum, Rashida Adeeb

doi:10.1016/j.asoc.2012.07.027

Cited by 142 publications

(45 citation statements)

References 14 publications

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“…In all experiments, the same parameters' settings as given in [12] are used. The weight vectors used in Eq.…”

Section: Resultsmentioning

confidence: 99%

“…7 is employed in the update scheme of CMOEA/D-DE [12] to solve CTP-series [4], [15] 1: Each new offspring y replaces n r parent solutions from the set P of its neighboring solutions as follows: 2: Set c = 0 and then do the following: 3: if c = n r or P = ∅ then 4: return; 5: else 6: Choose randomly an index j from P ; 7: Calculate the Tchebycheff aggregation function values of y and x j with the new objective values of Eq. 7;…”

Section: Multiobjective Evolutionary Algorithm Based On Decomposmentioning

confidence: 99%

“…Consequently, a chance is given to some good infeasible solutions with less degree of constraint violation to evolve during the evolutionary process. In [12], the framework of MOEA/D-DE [13], an improved variant of MOEA/D [14] is modified for CMOPs; The modified framework is denoted by CMOEA/D-DE [12]. Two penalty parameterless constraint handling techniques are employed in this framework to solve CTP-series [4], [15] and CF-series [16] test instances.…”

Section: Introductionmentioning

confidence: 99%

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Performance of a Constrained Version of MOEA/D on CTP-series Test Instances

Jan¹,

Khanum²,

Tairan³

et al. 2016

ijacsa

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Abstract-Constrained multiobjective optimization arises in many real-life applications, and is therefore gaining a constantly growing attention of the researchers. Constraint handling techniques differ in the way infeasible solutions are evolved in the evolutionary process along with their feasible counterparts. Our recently proposed threshold based penalty function gives a chance of evolution to infeasible solutions whose constraint violation is less than a specified threshold value. This paper embeds the threshold based penalty function in the update and replacement scheme of multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find tradeoff solutions for constrained multiobjective optimization problems (CMOPs). The modified algorithm is tested on CTP-series test instances in terms of the hypervolume metric (HV-metric). The experimental results are compared with the two well-known algorithms, NSGA-II and IDEA. The sensitivity of algorithm to the adopted parameters is also checked. Empirical results demonstrate the effectiveness of the proposed penalty function in the MOEA/D framework for CMOPs.

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“…In all experiments, the same parameters' settings as given in [12] are used. The weight vectors used in Eq.…”

Section: Resultsmentioning

confidence: 99%

Section: Multiobjective Evolutionary Algorithm Based On Decomposmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performance of a Constrained Version of MOEA/D on CTP-series Test Instances

Jan¹,

Khanum²,

Tairan³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…In our experiments, the same parameters' settings are used as in [16]. The weight vectors used in Eq.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…The framework of this algorithm is altered for CMOPs. The modified framework is denoted by CMOEA/D-DE (pleas see [16] for more details).…”

Section: Moea/d and Cmoea/d-de-tdamentioning

confidence: 99%

Threshold Based Penalty Functions for Constrained Multiobjective Optimization

Jan¹,

Tairan²,

Khanum³

et al. 2016

ijacsa

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A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

Asgarnezhad

Monadjemi

Aghaei

2021

Concurrency and Computation

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Sentiment classification is a field of sentiment analysis concerned with analyzing opinions, emotions, evaluations, and attitudes regarding a special topic like a product, an organization, a person, or an incident. With the growth of user-generated content on the Web, this field gained great importance in online reviews. With a wide range of reviews, customers cannot read all reviews. Considering the increasing rate of electronic documents and the urgent need manually mine for keywords that are hard and time-consuming, doing the same automatically is of high demand. A new framework proposed here to mine and classify users' comments based on mining keywords by applying the sequence pattern mining through the Separation-Power concept, a multi-objective evolutionary algorithm based on decomposition with four objectives, and a neural network as the final classifier. Some modifications are made on multi-objective evolutionary algorithm based on decomposition and Apriori algorithms to improve the text classification efficiency. To evaluate the proposed framework, three datasets applied; which compared with the two methods to measure accuracy, precision, recall, and error-index. The results indicate that this framework provides a better outcome than its counterparts with 99.45 precision, 99.34 accuracy, 99.48 recall, and 99.28% f-measure.

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A study of two penalty-parameterless constraint handling techniques in the framework of MOEA/D

Cited by 142 publications

References 14 publications

Performance of a Constrained Version of MOEA/D on CTP-series Test Instances

Performance of a Constrained Version of MOEA/D on CTP-series Test Instances

Threshold Based Penalty Functions for Constrained Multiobjective Optimization

A new hierarchy framework for feature engineering through multi‐objective evolutionary algorithm in text classification

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