A unified view of parallel multi-objective evolutionary algorithms

Talbi, El‐Ghazali

doi:10.1016/j.jpdc.2018.04.012

Cited by 29 publications

(22 citation statements)

References 27 publications

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“…The ranking and crowding mechanisms from the NSGA II [50] are the mostly used strategy in the area. However, the non-dominated set of solutions managed during the optimization procedure is generally structured as the centralized Pareto front [26,27]. This strategy is hard to achieve parallelism in the population level.…”

Section: Discussionmentioning

confidence: 99%

“…The linear weighted sum approach is taken for this application instead of the Pareto optimal solution for two reasons. Firstly, the most widely used parallel cellular model on GPUs is still immature for solving multi-objective problems where the main stream implementation manages a central Pareto front sequentially [26,27]. Second, most of the existing literature on the multi-objective job shop scheduling problems adopt the linear weighted sum approach [28] whose computational complexity is relatively lower.…”

Section: Mathematical Model Of Edjspmentioning

confidence: 99%

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Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Luo

Baz

Xue

et al. 2020

Future Generation Computer Systems

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Integrating energy savings into production efficiency is considered as one essential factor in modern industrial practice. A lot of research dealing with energy efficiency problems in the manufacturing process focuses solely on building a mathematical model within a static scenario. However, in the physical world shop scheduling problems are dynamic where unexpected events may lead to changes in the original schedule after the start time. This paper makes an investigation into minimizing the total tardiness, the total energy cost and the disruption to the original schedule in the job shop with new urgent arrival jobs. Because of the NP hardness of this problem, a dual heterogeneous island parallel genetic algorithm with the event driven strategy is developed. To reach a quick response in the dynamic scenario, the method we propose is made with a two-level parallelization where the lower level is appropriate for concurrent execution within GPUs or a multi-core CPU while codes from the two sides can be executed simultaneously at the upper level. In the end, numerical tests are implemented and display that the proposed approach can solve the problem efficiently. Meanwhile, the average results have been improved with a significant execution time decrease.

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Section: Discussionmentioning

confidence: 99%

Section: Mathematical Model Of Edjspmentioning

confidence: 99%

Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Luo

Baz

Xue

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

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“…Another way for reducing running time is paralleling and distributed computing (Luna and Alba 2015;Talbi 2019). To reduce the running time for fitness evaluation, the evaluations of the whole population are concurrently assigned by several processors in every iteration (Depolli et al 2013).…”

Section: Reducing Running Timementioning

confidence: 99%

A survey on evolutionary computation for complex continuous optimization

et al. 2021

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Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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“…Our second objective will be to test the ability of several multi-objective meta-heuristic to mine interesting association rules, in terms of computational efficiency and quality of returned laws. For such experiment we will choose state-of-the-art algorithms like genetic algorithms, genetic programming, firefly algorithms, PSO [16,[23][24][25][26][27][28][29][30] and possibly purpose our own. This experiment will allow us to choose the best algorithm to use in our project.…”

Section: Research Planmentioning

confidence: 99%

Extracting Interesting Sequence Association Rules from a High-Dimensional Healthcare Dataset using Multiobjective Metaheuristics

Berteloot

2021

Proceedings of the Canadian Conference on Artificial Intelligence

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A unified view of parallel multi-objective evolutionary algorithms

Cited by 29 publications

References 27 publications

Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

Solving the dynamic energy aware job shop scheduling problem with the heterogeneous parallel genetic algorithm

A survey on evolutionary computation for complex continuous optimization

Extracting Interesting Sequence Association Rules from a High-Dimensional Healthcare Dataset using Multiobjective Metaheuristics

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