Island models for cluster geometry optimization: how design options impact effectiveness and diversity

Leitão, António; Pereira, Francisco B.; Machado, Penousal

doi:10.1007/s10898-015-0302-7

Cited by 13 publications

(9 citation statements)

References 69 publications

(135 reference statements)

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“…The island model is no exception, as in other evolutionary algorithms, they must to set the parameters values for the mutation and crossing operators however, they must also set a series of additional parameters such as: migration rate (percentage of individuals to be migrated), the migration frequency, interconnection topology (how the islands communicate with one another), the size of the island, etc. 14,15 .…”

Section: Related Workmentioning

confidence: 99%

“…During the last years, the parallel island model has been successfully used, showing its robustness and applicability in different problems and areas of knowledge such as: Unequal Area Facility 22 , Job Shop Scheduling 23 , Cluster Geometry Optimization 15 , Parallel Harmony Search 24 , Multi-Objective problems 25 and problems of the molecular biology domain 26 .…”

Section: Related Workmentioning

confidence: 99%

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A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Arellano‐Verdejo¹,

Godoy-Calderón²,

Alonso-Pecina³

et al. 2017

IJCIS

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In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion mechanism, in its place a more efficient population-merging mechanism is used that dynamically reduces the population size as well as the total number of parallel evolving populations. Even more relevant is the fact that the proposed model incorporates an entropy measure to determine how to merge the populations such that no valuable information is lost during the evolutionary process. Extensive experimentation, using genetic algorithms over a well-known set of classical problems, shows the proposed model to be faster and more accurate than the traditional one.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Arellano‐Verdejo¹,

Godoy-Calderón²,

Alonso-Pecina³

et al. 2017

IJCIS

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show abstract

“…To maintain population diversity (enhancing the search diversity) and avoid premature convergence, a multipopulation genetic algorithm (MPGA) [4] is one feasible method where subpopulations are generated and individuals migrate periodically among them. In the evolutionary process, when intra-subpopulation evolution pushes individuals towards different local optima, migration can introduce new genes into the subpopulations [5]. As in [5,6], the migration can be implemented in different topologies that define how subpopulations are connected, which can influence the outcomes of MPGAs to some extent.…”

Section: Introductionmentioning

confidence: 99%

“…In the evolutionary process, when intra-subpopulation evolution pushes individuals towards different local optima, migration can introduce new genes into the subpopulations [5]. As in [5,6], the migration can be implemented in different topologies that define how subpopulations are connected, which can influence the outcomes of MPGAs to some extent.…”

Section: Introductionmentioning

confidence: 99%

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

Deng

Long

et al. 2020

Mathematical Problems in Engineering

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Populations of multipopulation genetic algorithms (MPGAs) parallely evolve with some interaction mechanisms. Previous studies have shown that the interaction structures can impact on the performance of MPGAs to some extent. This paper introduces the concept of complex networks such as ring-shaped networks and small-world networks to study how interaction structures and their parameters influence the MPGAs, where subpopulations are regarded as nodes and their interaction or migration of elites between subpopulations as edges. After solving the flexible job-shop scheduling problem (FJSP) by MPGAs with different parameters of interaction structures, simulation results were measured by criteria, such as success rate and average optimal value. The analysis reveals that (1) the smaller the average path length (APL) of the network is, the higher the propagation rate will be; (2) the performance of MPGAs increased first and then decreased along with the decrease of APL, indicating that, for better performance, the networks should have a proper APL, which can be adjusted by changing the structural parameters of networks; and (3) because the edge number of small-world networks remains unchanged with different rewiring possibilities of edges, the change in performance indicates that the MPGA can be improved by a more proper interaction structure of subpopulations as other conditions remain unchanged.

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“…These methods often come with high computational complexity and may be trapped in local dilemma. Currently, island model [16] is widely accepted as one of the most successful ways of maintaining diversity. This method is proposed to maintain sub-population in each island and individuals from different islands may migrate from one to the other.…”

Section: Introductionmentioning

confidence: 99%

Dynamic island model based on spectral clustering in genetic algorithm

Meng¹,

Wu²,

Ellis³

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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How to maintain relative high diversity is important to avoid premature convergence in population-based optimization methods. Island model is widely considered as a major approach to achieve this because of its flexibility and high efficiency.The model maintains a group of sub-populations on different islands and allows sub-populations to interact with each other via predefined migration policies. However, current island model has some drawbacks. One is that after a certain number of generations, different islands may retain quite similar, converged subpopulations thereby losing diversity and decreasing efficiency. Another drawback is that determining the number of islands to maintain is also very challenging. Meanwhile initializing many sub-populations increases the randomness of island model. To address these issues, we proposed a dynamic island model (DIM-SP) which can force each island to maintain different subpopulations, control the number of islands dynamically and starts with one sub-population. The proposed island model outperforms the other three state-of-the-art island models in three baseline optimization problems including job shop scheduler problem, travelling salesmen problem and quadratic multiple knapsack problem.

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Island models for cluster geometry optimization: how design options impact effectiveness and diversity

Cited by 13 publications

References 69 publications

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

A New Efficient Entropy Population-Merging Parallel Model for Evolutionary Algorithms

Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

Dynamic island model based on spectral clustering in genetic algorithm

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