Effective local evolutionary searches distributed on an island model solving bi-objective optimization problems

Cheshmehgaz, Hossein Rajabalipour; Desa, Mohammad Ishak; Wibowo, Antoni

doi:10.1007/s10489-012-0375-7

Cited by 23 publications

(6 citation statements)

References 17 publications

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“…As a final point, further work will focus on the design of more advanced and relatively 'unexplored' hybrid models such as the 'island model' and others from the field of parallel metaheuristics (see e.g. Talbi 2009, Cheshmehgaz et al 2013, Dulebenets 2020Xavier et al 2021).…”

Section: Discussionmentioning

confidence: 99%

Resource leveling optimization by hybrid metaheuristics

Aristotelous

Nearchou

2023

Preprint

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Consideration is given to the heuristic solution of the resource leveling problem (RLP) in project scheduling with limited resources. The objective is to minimize the changes in the level of resource usage from period to period over the planning horizon of the project while keeping the project duration fixed. First, we present two novel greedy schedule algorithms for the RLP solution. The performance of the proposed algorithms are investigated as low-level hybrids in the context of three famous population-based heuristics namely, differential evolution (DE), genetic algorithm (GA) and particle swarm optimization (PSO). Then, we additionally present two new high-level hybridization schemes (HS), referred to herein as parallel and serial HS respectively which combine DE, GA and PSO in a single hybrid solution algorithm. Detailed experimentation over known complex data sets measures the efficiency of the new hybrids. Statistical analysis employed rank the hybrids according to their solution efficiency. Moreover, comparisons between the developed best hybrid and commercial project management software show a substantial higher performance for the former over real-world construction projects.

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Section: Discussionmentioning

confidence: 99%

Resource leveling optimization by hybrid metaheuristics

Aristotelous

Nearchou

2023

Preprint

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“…The experimental results of developed MDE on 6 complex unconstrained benchmark functions equated with DE/rand/1 (9) , DE/best/1 (29) , DE/target-to-best/1 (30) , GDE (31) and PSODE (32) . Mean and standard deviation (SD) over 30 trail runs of MDE with other methods are stated in Table 2.…”

Section: Validation Of Suggested Mdementioning

confidence: 99%

Design Optimization Using Modified Differential Evolution Algorithm

Tiwari,

Mishra,

Parouha

2023

IJST

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Objectives:The key objective of this article is to suggest a modified differential evolution (MDE) algorithm for design problem optimization particularly reactor network design (RND) problem. Methods: During the few decades differential evolution (DE) algorithm achieved noticeable progress and solved a wide variety of optimization issues. However, the DE suffers from low diversification, poor exploration ability and stagnation. Hence, using concept of the particle swarm optimization mechanism (PSO), suggested MDE employed new mutation operator, to balance exploitation and exploration activities. Also, on the basis of time-varying scheme new mutation operator integrates new control parameter, to avoid stagnation. A group of 6 unconstrained benchmark functions are solved, to investigate the presentation of MDE algorithm. Moreover, its practical superiority is further verified by solving RND problem. Findings: The experiential results show that the suggested MDE performs well in each case of unconstrained benchmark functions with the highest rate of success. Moreover, optimize the RND problem very effectively with the lowest time (2.98s) and fewer number of function evaluations (12729). Furthermore, outcomes suggest that the proposed MDE exhibits a better or at least competitive performance compared to evolutionary algorithms. Novelty: The exploitation and exploration ability of the suggested MDE are balanced efficiently due to use of memory facts (i.e. novel mutation operator) and adapted (i.e. new time-varying) control parameters.

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“…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%

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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Effective local evolutionary searches distributed on an island model solving bi-objective optimization problems

Cited by 23 publications

References 17 publications

Resource leveling optimization by hybrid metaheuristics

Resource leveling optimization by hybrid metaheuristics

Design Optimization Using Modified Differential Evolution Algorithm

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

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