An ensemble framework for assessing solutions of interval programming problems

Sun, Jing; Gong, Dunwei; Zeng, Xiao‐Jun; Geng, Nan

doi:10.1016/j.ins.2018.01.006

Cited by 26 publications

(11 citation statements)

References 46 publications

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“…As a result, users can utilize the proposed ensemble dominance to handle their interval programming models. Moreover, they will free themselves from choosing an appropriate approach to assess solutions [41], [42].…”

Section: Interval Multi-objective Evolutionary Optimizationmentioning

confidence: 99%

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Gong

Zhang

et al. 2020

IEEE Trans. Evol. Computat.

Self Cite

148

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Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances.

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Section: Interval Multi-objective Evolutionary Optimizationmentioning

confidence: 99%

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Gong

Zhang

et al. 2020

IEEE Trans. Evol. Computat.

Self Cite

148

View full text Add to dashboard Cite

show abstract

“…In the evolutionary computing field, many optimization problems have widely been treated by using evolutionary algorithms, such as [4][5][6] and also [7][8][9]. In this research line, the parameter setting approach [10] is one of the most studied challenges [11][12][13][14][15][16][17].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Black Hole Algorithm for Solving the Set Covering Problem

Soto

Crawford

Olivares

et al. 2018

Mathematical Problems in Engineering

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Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics.

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“…Based on the basic CHTs, many concepts like cooperative coevolution [4], [5] and ensemble [6], [7] have been proposed. And some researchers tried to solve the problems from some other aspects, e.g., problem characteristics [8], [9].…”

Section: Introductionmentioning

confidence: 99%

On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

2020

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Cooperation between Evolutionary Algorithms (EAs) and Constraint Handling Techniques (CHTs) plays an important role in Constrained Optimization Evolutionary Algorithms (COEAs). A constrained composite differential evolution (C 2 oDE) sets a good example on the cooperation. This paper tries to further study the inner mechanism, i.e., the effect of different methods on generating and selecting solutions. In the solution generating part, a new method, which adopts stochastic ranking in selecting the best individual in the DE mutation operators, is proposed. In the solution selecting part, a new constraint handling technique combination is added. The experiments on benchmark functions from IEEE CEC2006 verify the effect. During the experiment, it is found that a new cooperation manner performs better than C 2 oDE, which reflects the importance of cooperation. INDEX TERMS constrained optimization, constraint handling techniques, differential evolution, cooperation manner.

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An ensemble framework for assessing solutions of interval programming problems

Cited by 26 publications

References 46 publications

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

A Similarity-Based Cooperative Co-Evolutionary Algorithm for Dynamic Interval Multiobjective Optimization Problems

Adaptive Black Hole Algorithm for Solving the Set Covering Problem

On the Cooperation Between Evolutionary Algorithms and Constraint Handling Techniques: A Further Empirical Study

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