Adaptive Multimodal Continuous Ant Colony Optimization

Yang, Qiang; Chen, Wei-Neng; Yu, Zhengtao; Gu, Tianlong; Li, Yun; Zhang, Huaxiang; Zhang, Jun

doi:10.1109/tevc.2016.2591064

Cited by 271 publications

(84 citation statements)

References 81 publications

(143 reference statements)

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“…Yet as a base, they may properly be combined with other techniques to deal with such problems well. Commonly used algorithms include Genetic Algorithm (GA) [17], Particle Swarm Optimization (PSO) [18], Ant Colony Optimization (ACO) [19] and Differential Evolution (DE) [20]. The Big Bang-Big Crunch (BB-BC) algorithm is improved for multimodal optimization because of its low computational cost [21].…”

Section: > Replace This Line With Your Paper Identification Number mentioning

confidence: 99%

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Huang

Ding

Zhou

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This work proposes an optimization strategy for multi-robot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this work presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A pre-judgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed guiding mutation operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.Index Terms-Multi-robot task allocation, multimodal optimization, niching immune-based optimization algorithm, Softmax regression, guiding mutation operator.

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Section: > Replace This Line With Your Paper Identification Number mentioning

confidence: 99%

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Huang

Ding

Zhou

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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“…Compared with other methods, ACO and ACO R are more robust for problems with many local optima. ACO R can also deal with complex multimodal problems effectively [32]. These reasons all motivate us to adopt ACO R as an optimizer in this paper.…”

Section: Related Workmentioning

confidence: 99%

A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

Zhang

et al. 2019

Remote Sensing

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Automatic image registration for multi-sensors has always been an important task for remote sensing applications. However, registration for images with large resolution differences has not been fully considered. A coarse-to-fine registration strategy for images with large differences in resolution is presented. The strategy consists of three phases. First, the feature-base registration method is applied on the resampled sensed image and the reference image. Edge point features acquired from the edge strength map (ESM) of the images are used to pre-register two images quickly and robustly. Second, normalized mutual information-based registration is applied on the two images for more accurate transformation parameters. Third, the final transform parameters are acquired through direct registration between the original high- and low-resolution images. Ant colony optimization (ACO) for continuous domain is adopted to optimize the similarity metrics throughout the three phases. The proposed method has been tested on image pairs with different resolution ratios from different sensors, including satellite and aerial sensors. Control points (CPs) extracted from the images are used to calculate the registration accuracy of the proposed method and other state-of-the-art methods. The feature-based preregistration validation experiment shows that the proposed method effectively narrows the value range of registration parameters. The registration results indicate that the proposed method performs the best and achieves sub-pixel registration accuracy of images with resolution differences from 1 to 50 times.

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“…Obviously niching can be introduced to other meta-heuristics as well, such as Artificial Immune Systems (AIS) [60], [61], Ant Colony Optimization (ACO) [62]- [64], and Cultural Algorithms (CA) [65]. It is also possible to induce niching behaviour through probalistic modeling building, e.g., via an Estimated Distributed Algorithm (EDA) [66].…”

Section: Other Meta-heuristicsmentioning

confidence: 99%

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

Epitropakis

Deb

et al. 2017

IEEE Trans. Evol. Computat.

263

101

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Abstract-Multi-modal optimization (MMO) aiming to locate multiple optimal (or near-optimal) solutions in a single simulation is an important topic which has practical relevance in problem solving across many fields. Population-based meta-heuristics have been shown particularly effective in solving MMO prolems, if equipped with specifically-designed diversity-preserving mechanisms, commonly known as niching methods. This paper provides an updated survey on niching methods. The paper first revisits the fundamental concepts about niching and its most representative schemes, then reviews the more recent development of niching methods, including novel and hybrid methods, performance measures, and benchmarks for their assessment. Furthermore, the paper surveys previous attempts on leveraging the capabilities of niching to facilitate various optimization (e.g., multi-objective and dynamic optimization) and machine learning (e.g., clustering, feature selection, and learning ensembles) tasks. A list of successful applications of niching methods to real-world problems is provided to demonstrate that niching methods are able to provide solutions that are difficult for conventional optimization methods to offer. The significant practical value of niching methods is clearly exemplified through these applications. Finally, the paper poses challenges and research questions on niching that are yet to be appropriately addressed. Providing answers to these questions is crucial before we can bring more fruitful benefits of niching to real-world problem solving.

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Adaptive Multimodal Continuous Ant Colony Optimization

Cited by 271 publications

References 81 publications

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

Multiple-Solution Optimization Strategy for Multirobot Task Allocation

A Coarse-to-Fine Registration Strategy for Multi-Sensor Images with Large Resolution Differences

Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications

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