A Survey on Cooperative Co-Evolutionary Algorithms

Ma, Xiaoliang; Li, Xiaodong; Zhang, Qingfu; Tang, Ke; Liang, Zhengping; Xie, Weixin; Zhu, Zexuan

doi:10.1109/tevc.2018.2868770

Cited by 225 publications

(79 citation statements)

References 199 publications

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“…CEA searches the optimal solution cooperatively through decomposing the whole solution space into various sub solution spaces. Therefore, the decomposition strategy is significant because the performance of CEA is sensitive to the decomposition status [26]. Various researchers studied the decomposition strategies [27][28][29].…”

Section: Methodologies Distribution(s) Objective(s) (Min)mentioning

confidence: 99%

“…Then, the sth sub-component of p best (ϕ) is replaced with s th sub-component of sub ind (ϕ) (line: [13][14][15]. sg best (ϕ) (line: [16][17][18] and g best (line: [25][26][27] are updated in the same manner. CEA works for each sub-component for t generations in order to maintain the best historical information and search the optimal solution.…”

Section: Cea Evaluationmentioning

confidence: 99%

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Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Sun

Lin

et al. 2019

Mathematics

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Flexible job shop scheduling is an important issue in the integration of research area and real-world applications. The traditional flexible scheduling problem always assumes that the processing time of each operation is fixed value and given in advance. However, the stochastic factors in the real-world applications cannot be ignored, especially for the processing times. We proposed a hybrid cooperative co-evolution algorithm with a Markov random field (MRF)-based decomposition strategy (hCEA-MRF) for solving the stochastic flexible scheduling problem with the objective to minimize the expectation and variance of makespan. First, an improved cooperative co-evolution algorithm which is good at preserving of evolutionary information is adopted in hCEA-MRF. Second, a MRF-based decomposition strategy is designed for decomposing all decision variables based on the learned network structure and the parameters of MRF. Then, a self-adaptive parameter strategy is adopted to overcome the status where the parameters cannot be accurately estimated when facing the stochastic factors. Finally, numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm and show the superiority compared with the state-of-the-art from the literature.Mathematics 2019, 7, 318 2 of 20 optimization problem with two sub-problems, i.e., operation sequence and machine assignment, and was proved as an NP-hard problem [9]. MotivationVarious researchers concentrated on the effective algorithms for minimizing the maximum completion time which named makespan of FJSP. Lin and Gen provided a survey in 2018 and presented the information of scheduling, e.g., mathematical formulation and graph representations [10]. Chaudhry and Khan reviewed solution techniques and methods published for flexible scheduling and presented an overview of the research trend of flexible scheduling in 2016 [11]. However, most of the existing research assumes that the processing time of each operation on the corresponding machine is fixed and given in advance. In actual, the stochastic factors in the real-world applications cannot be ignored. In addition, the processing time appears to be particularly important [12]. Therefore, in this paper, we consider the stochastic FJSP (S-FJSP), and the stochastic processing time is modeled as three probability distributions, i.e., the normal distribution, the Gaussian distribution and the exponential distribution.In recent years, various research was studied for stochastic scheduling problems. As listed in Table 1, Lei proposed a simplified multi-objective genetic algorithm (MOGA) for minimizing the expected makespan of JSP [13]. Hao et al. proposed an effective multi-objective estimation of distribution algorithm (moEDA) for minimizing the expected makespan and the total tardiness of JSP [14]. Kundakc and Kulak proposed a hybrid evolutionary algorithm for minimizing the expected of JSP makespan as well [15]. Represented by the above studies, some researchers modelled the stochastic processing timess by the unifor...

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Section: Methodologies Distribution(s) Objective(s) (Min)mentioning

confidence: 99%

Section: Cea Evaluationmentioning

confidence: 99%

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Sun

Lin

et al. 2019

Mathematics

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“…1.76e+09 1.21e+08 1.24e+12 5.15e+11 1.67e+09 1.08e+08 9.84e+11 2.30e+11 1.59e+09 7.11e+07 1.75e+09 1.38e+08 f l, 8 5.64e+07 2.73e+06 1.76e+12 8.62e+11 2.52e+07 1.36e+06 3.38e+12 1.50e+12 2.22e+07 1.48e+06 1.88e+08 1.32e+08 f l, 9 1.69e+08 1.55e+07 1.16e+12 3.32e+11 1.11e+08 4.10e+06 1.33e+12 2.19e+11 1.05e+08 5.30e+06 3.26e+08 2.60e+08 f l,10 1.07e+08 3.10e+06 1.45e+12 4.33e+11 2.01e+09 6.03e+09 1.80e+12 3.31e+11 8.27e+07 2.49e+06 2.27e+09 2.53e+09 variables into one component, thus all decision variables result in one group for overlapping problems. By decomposing overlapping problems into components that are optimized cooperatively, RDG3 is able to greatly improve the solution quality.…”

Section: Comparison On Overlapping Problemsmentioning

confidence: 99%

“…The structure of decision variable interactions can be used to decompose a large-scale problem into sub-problems, that are solved in a cooperative way. Such a divide-and-conquer approach is known as Cooperative Co-evolution (CC) [7], and has achieved many successes in the context of large-scale global optimization [6], [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Decomposition for Large-scale Optimization Problems with Overlapping Components

Sun

Ernst

et al. 2019

2019 IEEE Congress on Evolutionary Computation (CEC)

Self Cite

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“…During the optimization process, CCAs are usually composed of two or more populations, which evolve simultaneously by applying different objectives or search methods and allow interaction, trying to obtain a global solution after combining the respective final solutions together. More related works along CCA can be found in [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data

Kang

Zhang

2020

Entropy

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Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.

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A Survey on Cooperative Co-Evolutionary Algorithms

Cited by 225 publications

References 199 publications

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Cooperative Co-Evolution Algorithm with an MRF-Based Decomposition Strategy for Stochastic Flexible Job Shop Scheduling

Decomposition for Large-scale Optimization Problems with Overlapping Components

A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data

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