Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization

Peng, Xingguang; Jin, Yaochu; Wang, Handing

doi:10.1109/tcyb.2018.2846179

Cited by 47 publications

(12 citation statements)

References 59 publications

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“…SLPSO was proposed by the same authors in [22], where a social learning concept is employed. For MMOCC, which is currently proposed by Peng etc, which adopts the idea of CC framework and the techniques of multi-modular optimization [26].…”

Section: Experiments and Discussionmentioning

confidence: 99%

An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

et al. 2019

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Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

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Section: Experiments and Discussionmentioning

confidence: 99%

An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

et al. 2019

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“…The path planning based on genetic algorithm can be simply summarized into three steps: coding, genetic manipulation and decoding, 12 which is outlined in Figure 2. The genetic operation is divided into steps of selection, crossover, and variation.…”

Section: The Genetic Algorithmmentioning

confidence: 99%

An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

Zheng

Jiang

et al. 2019

International Journal of Advanced Robotic Systems

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This article presents a comparison of different path-planning algorithms for autonomous underwater vehicles using terrain-aided navigation. Four different path-planning methods are discussed: the genetic algorithm, the A* algorithm, the rapidly exploring random tree* algorithm, and the ant colony algorithm. The goal of this article is to compare the four methods to determine how to obtain better positioning accuracy when using terrain-aided navigation as a means of navigation. Each algorithm combines terrain complexity to comprehensively consider the motion characteristics of the autonomous underwater vehicles, giving reachable path between the start and end points. Terrain-aided navigation overcomes the challenges of underwater domain, such as visual distortion and radio frequency signal attenuation, which make landmark-based localization infeasible. The path-planning algorithms improve the terrain-aided navigation positioning accuracy by considering terrain complexity. To evaluate the four algorithms, we designed simulation experiments that use real-word seabed bathymetry data. The results of autonomous underwater vehicle navigation by terrain-aided navigation in these four cases are obtained and analyzed.

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“…Recently, a multi-modal optimization based on CC (MMO-CC) was proposed in [40], MMO-CC searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents.…”

Section: Related Workmentioning

confidence: 99%

LSHADE-SPA memetic framework for solving large-scale optimization problems

Hadi

Mohamed

Jambi

2018

Complex Intell. Syst.

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During the last decade, large-scale global optimization has been one of the active research fields. Optimization algorithms are affected by the curse of dimensionality associated with this kind of complex problems. To solve this problem, a new memetic framework for solving large-scale global optimization problems is proposed in this paper. In the proposed framework, success history-based differential evolution with linear population size reduction and semi-parameter adaptation (LSHADE-SPA) is used for global exploration, while a modified version of multiple trajectory search is used for local exploitation. The framework introduced in this paper is further enhanced by the concept of divide and conquer, where the dimensions are randomly divided into groups, and each group is solved separately. The proposed framework is evaluated using IEEE CEC2010 and the IEEE CEC2013 benchmarks designed for large-scale global optimization. The comparison results between our framework and other state-of-the-art algorithms indicate that our proposed framework is competitive in solving large-scale global optimization problems.

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Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization

Cited by 47 publications

References 59 publications

An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

An evaluation of path-planning methods for autonomous underwater vehicle based on terrain-aided navigation

LSHADE-SPA memetic framework for solving large-scale optimization problems

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