A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems

Tang, Lixin; Wang, Xianpeng

doi:10.1109/tevc.2012.2185702

Cited by 154 publications

(55 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the other parameters are set as described in the comparison algorithm. Three evaluation criteria [8] as two set coverage (C), generational distance metric (GD) and space metric (SP) are used together to test the performance of each algorithm. For fair comparison, each algorithm is run independently 40 times and the algorithm stops when the number of function evaluation per run reaches 25000.…”

Section: Constrained Multi-objective Optimization Algorithm Based On mentioning

confidence: 99%

Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation

Wang¹,

Cao²

2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

View full text Add to dashboard Cite

A new constrained multi-objective optimization algorithm based on ε adaptive weighted constraint violation, AW-CMOA, is proposed to solve the constrained multi-objective optimization problems. Considering different levels of difficulty in the satisfaction of varied constraints, a new ε constraint handling strategy for multi-objective optimization, in which the constraint violation is redefined and the self-adapting ε level parameter is set, is designed to make the constraint violation for individual to reflect its true quality more objectively and accurately. Simultaneously, according to the features of the constrained multi-objective and the evolutionary mechanism of BBO, the model of constrained multi-objective optimization applicable to BBO is built. In the model, the habitat suitability index, in combination with the degree of feasible and the Pareto dominance relation between the individuals, is redefined. Moreover, the self-adaptive method of determining the migration rate is designed to improve the ability for exploitation and the utilization of better individual. Numerical experiments have shown that the proposed algorithm is competitive to other constrained multi-objective optimization algorithms in terms of convergence and distribution, and is capable of solving the complex constrained multi-objective optimization problems more effectively and efficiently.

show abstract

Section: Constrained Multi-objective Optimization Algorithm Based On mentioning

confidence: 99%

Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation

Wang¹,

Cao²

2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

View full text Add to dashboard Cite

show abstract

“…. , }, the maximum iterations max , the selection probability of each neighborhood to be fl 1/ , the iteration number fl 0, the external archive to be empty, and = 0. while ≤ //q is the sum of candidate neighborhoods (5) fl ℎ( 0 , ) //perform multi-objective local search on 0 in and obtains a set of non-dominated solutions (6) fl ℎ V ( , ) //update the external archive with (7) fl + 1 (8) end while (9) while < max (10) fl ℎ ℎ ( ) //select a neighborhood based on selection probabilities (11) Multi-objective local search: Phase I -neighborhood search: (12) fl ( ) //randomly select a solution from (13) fl ℎ ( , ) //a random solution is generated in neighborhood (14) 1 fl ℎ( , ) //perform multi-objective local search on in (15) Multi-objective local search: Phase II -path relinking:…”

Section: Pabovns Algorithmmentioning

confidence: 99%

A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Tian

Liu

2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Different from most researches focused on the single objective hybrid flowshop scheduling (HFS) problem, this paper investigates a biobjective HFS problem with sequence dependent setup time. The two objectives are the minimization of total weighted tardiness and the total setup time. To efficiently solve this problem, a Pareto-based adaptive biobjective variable neighborhood search (PABOVNS) is developed. In the proposed PABOVNS, a solution is denoted as a sequence of all jobs and a decoding procedure is presented to obtain the corresponding complete schedule. In addition, the proposed PABOVNS has three major features that can guarantee a good balance of exploration and exploitation. First, an adaptive selection strategy of neighborhoods is proposed to automatically select the most promising neighborhood instead of the sequential selection strategy of canonical VNS. Second, a two phase multiobjective local search based on neighborhood search and path relinking is designed for each selected neighborhood. Third, an external archive with diversity maintenance is adopted to store the nondominated solutions and at the same time provide initial solutions for the local search. Computational results based on randomly generated instances show that the PABOVNS is efficient and even superior to some other powerful multiobjective algorithms in the literature.

show abstract

“…Tang et al (2014b). Dealing with multiple criteria, three approaches may be used, including aggregating approaches, population-based approaches and Pareto-based approaches (Tang and Wang 2013). In this research, we focus on finding Pareto front solutions, as the search space is very large and exact methods will be so time-consuming for largesized instances.…”

Section: Solution Approachmentioning

confidence: 99%

Coordinative production and maintenance scheduling problem with flexible maintenance time intervals

Khatami

Zegordi

2014

J Intell Manuf

View full text Add to dashboard Cite

This study investigates the simultaneous scheduling of production and planning of maintenance activities in the flow shop scheduling environment. The problem is considered in a bi-objective form, minimizing the makespan as the production scheduling criterion and minimizing the system unavailability as the maintenance planning criterion. We propose the coordinative production and maintenance scheduling model in which the time interval between consecutive maintenance activities as well as the number of maintenance activities on each machine are assumed to be non-fixed. The coordinative model aims to find the best permutation of jobs as the production problem and to assign the maintenance activities into the schedule as the maintenance problem, simultaneously. Moreover, a special setting called single server maintenance is introduced and discussed. A biobjective ant colony system algorithm is presented to solve the problem in focus, introducing some novel ideas. CDS and NEH heuristics are applied to define the heuristic information part of the proposed algorithm. Some experiments are carried out to select the appropriate heuristic method between CDS and NEH. Moreover, some experiments are performed using the well-known Taillard benchmark, comparing the performance of the proposed algorithm with another ant colony optimization algorithm. Computational experiments indicate the effectiveness of the proposed algorithm.

show abstract

A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems

Cited by 154 publications

References 42 publications

Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation

Constrained Multi-objective Optimization Algorithm Based on ε Adaptive Weighted Constraint Violation

A Pareto-Based Adaptive Variable Neighborhood Search for Biobjective Hybrid Flow Shop Scheduling Problem with Sequence-Dependent Setup Time

Coordinative production and maintenance scheduling problem with flexible maintenance time intervals

Contact Info

Product

Resources

About