Deterministic metaheuristic based on fractal decomposition for large-scale optimization

Nakib, Amir; Ouchraa, Salima; Shvai, Nadiya; Souquet, Léo; Talbi, El‐Ghazali

doi:10.1016/j.asoc.2017.07.042

Cited by 20 publications

(10 citation statements)

References 16 publications

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“…The Fractal Decomposition Algorithm [23] (FDA) is a divide-and-conquer based algorithm that has been designed to solve large-scale single objective continuous optimization problems. FDA builds a search tree of promising optimum areas of a depth k (called Fractal depth), by dividing the search space recursively using geometrical hyperspheres.…”

Section: Fractal Decomposition Algorithm : a Recallmentioning

confidence: 99%

“…It was designed to find the most promising regions. To do so, their attractiveness is approximated as in [23]. Then, all sub-hyperspheres are sorted by their scores, and the best one is selected to be further decomposed.…”

Section: Fractal Decomposition Algorithm : a Recallmentioning

confidence: 99%

“…The idea behind Mo-FDA-D (Multi-Objective Fractal Decomposition Algorithm Dominance Based) is to keep the structure of the framework provided by the original version of FDA [23], i.e. the geometric fractal decomposition as well as the geometric form and the different phases composing the original version of FDA.…”

Section: Dominance-based Fractal Decompositionmentioning

confidence: 99%

“…We are interested in tackling MOPs using a decomposition of the decision space. In our previous work, we proposed a new metaheuristic for single objective optimization called Fractal Decomposition Algorithm (FDA) [23] which is based on hyperspheres Fractals. It is a deterministic metaheuristic developed to solve large-scale continuous optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

Souquet¹,

Talbi²,

Nakib

2020

IEEE Access

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Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decision space using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA's properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.

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Section: Fractal Decomposition Algorithm : a Recallmentioning

confidence: 99%

Section: Fractal Decomposition Algorithm : a Recallmentioning

confidence: 99%

Section: Dominance-based Fractal Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

Souquet¹,

Talbi²,

Nakib

2020

IEEE Access

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“…This paper deals with these problems by using a new decomposition-based algorithm called: "Fractal geometric decomposition base algorithm" (FDA). It is a deterministic metaheuristic developed to solve largescale continuous optimization problems [5]. It can be noticed, that we call large scale problems those having the dimension greater than 1000.…”

Section: Introductionmentioning

confidence: 99%

Multi-node environment strategy for Parallel Deterministic Multi-Objective Fractal Decomposition

Souquet,

Nakib

2019

Preprint

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This paper presents a new implementation of deterministic multiobjective (MO) optimization called "Multiobjective Fractal Decomposition Algorithm" (Mo-FDA). The original algorithm was designed for monoobjective large-scale continuous optimization problems. It is based on a "divide-and-conquer" strategy and a geometric fractal decomposition of the search space using hyperspheres. Then, to deal with MO problems a scalarization approach is used. In this work, a new approach has been developed on a multi-node environment using containers. The performance of Mo-FDA was compared to state of the art algorithms from the literature on classical benchmark of multi-objective optimization.

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Investigating Fractal Decomposition Based Algorithm on Low-Dimensional Continuous Optimization Problems

Llanza

Shvai

Nakib

2023

Lecture Notes in Computer Science

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Deterministic metaheuristic based on fractal decomposition for large-scale optimization

Cited by 20 publications

References 16 publications

Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

Multi-node environment strategy for Parallel Deterministic Multi-Objective Fractal Decomposition

Investigating Fractal Decomposition Based Algorithm on Low-Dimensional Continuous Optimization Problems

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