Graph-based solution batch management for Multi-Objective Evolutionary Algorithms

Mateo, Pedro M.; Alberto, Isolina

doi:10.1016/j.asoc.2017.10.042

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…▪Use the network to control individuals. Mateo et al [20] ▪Directed domination graphs ▪Use directed domination graphs to represent the domination relations between individuals in the population, obtaining a multi-objective evolutionary algorithm with an improved performance. ▪Use the network to control individuals.…”

Section: Studies Network Descriptionsmentioning

confidence: 99%

“…An evolutionary algorithm designed by a scale-free network tends to perform better when the number of optimization objectives increases. In [20], a multi-objective evolutionary algorithm with improved performance was designed using control graphs. Additionally, in our previous research [17], we used seven different networks to design MPGAs and then studied how different network topologies affected the performance of the MPGAs.…”

Section: Introductionmentioning

confidence: 99%

“…As summarized in Table 1, in the existing literature, mainly regular [10][11][12][13], scale-free [15][16][17][18], random [18], and small-world networks [18] have been used to study GA or other evolutionary algorithms. In addition, these networks have been mostly used to control propagation behaviors of advantageous genes between individuals [13,15,16,18,20], and have rarely been used to control the propagation behaviors of advantageous genes among sub-populations. Therefore, it is still not clear how the propagation behavior (this study mainly refers to the propagation rate) of advantageous genes among sub-populations affects the performance of (Grant No.…”

Section: Introductionmentioning

confidence: 99%

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Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Shi

Long

et al. 2020

PLoS ONE

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A genetic algorithm (GA) cannot always avoid premature convergence, and multi-population is usually used to overcome this limitation by dividing the population into several sub-populations (sub-population number) with the same number of individuals (sub-population size). In previous research, the questions of how a network structure composed of sub-populations affects the propagation rate of advantageous genes among sub-populations and how it affects the performance of GA have always been ignored. Therefore, we first propose a multi-population GA with an ER network (MPGA-ER). Then, by using the flexible job shop scheduling problem (FJSP) as an example and considering the total individual number (TIN), we study how the sub-population number and size and the propagation rate of advantageous genes affect the performance of MPGA-ER, wherein the performance is evaluated by the average optimal value and success rate based on TIN. The simulation results indicate the following regarding the performance of MPGA-ER: (i) performance shows considerable improvement compared with that of traditional GA; (ii) for an increase in the sub-population number for a certain TIN, the performance first increases slowly, and then decreases rapidly; (iii) for an increase in the sub-population size for a certain TIN, the performance of MPGA-ER first increases rapidly and then tends to remain stable; and (iv) with an increase in the propagation rate of advantageous genes, the performance first increases rapidly and then decreases slowly. Finally, we use a parameter-optimized MPGA-ER to solve for more FJSP instances and demonstrate its effectiveness by comparing it with that of other algorithms proposed in other studies.

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Section: Studies Network Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Shi

Long

et al. 2020

PLoS ONE

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“…Kirley and Stewart [45] used scale-free, small-world, and random networks to limit possible crossover partners in a population and then studied the performance of differently obtained algorithms on multiobjective optimization problems. Mateo and Alberto [46] used directed domination graphs to represent the domination relations between individuals in the population, obtaining a multiobjective evolutionary algorithm with an improved performance. e influence of network structures on the selection pressures of algorithms was also studied theoretically by Allen et al [47].…”

Section: Introductionmentioning

confidence: 99%

Multipopulation GA/IWO with Coupled Scale-Free Networks for Solving Flexible Job-Shop Scheduling Problems

Shi

Deng

Yin

et al. 2022

Mathematical Problems in Engineering

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In recent years, an increasing number of population-based metaheuristic algorithms have been proposed to solve the flexible job-shop scheduling problem (FJSP) according to its practicality and complexity. Most of these algorithms are single-population-based and hence are very susceptible to becoming trapped in local optimal values. Thus, multipopulation methods are widely used to improve these algorithms, resulting in multipopulation algorithms. These multipopulation algorithms have been widely studied in the context of single-layer complex networks recently. However, coupled networks used to control two (or more) algorithms simultaneously to get a better algorithm are always ignored in literature. Therefore, in this paper, using coupled scale-free networks (with different scaling exponents) to control the genetic algorithm (GA) and the invasive weed optimization (IWO) simultaneously, a multipopulation GA/IWO with coupled scale-free networks (MPGAIWO-SF) is proposed to solve FJSP. Then, we study how some parameters (e.g., subpopulation size, subpopulation number, and scaling exponent) of MPGAIWO-SF affect its performance, which is also ignored in literature. The simulation results illustrate that (1) the performance of MPGAIWO-SF is significantly improved compared with that of GA and IWO; (2) as the subpopulation size increases, the performance of MPGAIWO-SF first becomes better and then remains almost unchanged; (3) as the subpopulation number increases, the performance of MPGAIWO-SF first becomes better and then decreases rapidly; and (4) the performance of MPGAIWO-SF becomes worse slightly as the scaling exponent decreases. Finally, solving more FJSP instances, the MPGAIWO-SF with optimized parameters is compared with other related algorithms to verify its effectiveness.

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Research on the performance of multi-population genetic algorithms with different complex network structures

Shi

Long

et al. 2020

Soft Comput

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Graph-based solution batch management for Multi-Objective Evolutionary Algorithms

Cited by 4 publications

References 13 publications

Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems

Multipopulation GA/IWO with Coupled Scale-Free Networks for Solving Flexible Job-Shop Scheduling Problems

Research on the performance of multi-population genetic algorithms with different complex network structures

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