A new indicator-based many-objective ant colony optimizer for continuous search spaces

Falcón-Cardona, Jesús Guillermo; Coello, Carlos A. Coello

doi:10.1007/s11721-017-0133-x

Cited by 33 publications

(10 citation statements)

References 40 publications

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“…In their study, Wang et al combined ant colony algorithms with optimal support vector machine algorithms to improve the coding algorithm and optimize the design of parameters and features to improve the adaptive nature of the algorithm [10]. In their study, Falcón-Cardona et al proposed a continuous search space multiobjective ant colony optimization algorithm for continuous multiobjective optimization problems, which showed strong competitiveness in most of the measurement metrics [11]. Ye et al proposed an improved algorithm with a negative feedback mechanism, using search history information, continuously acquiring failure experience, optimally exploring the unknown space, and identifying high-quality solutions superior to other algorithms [12].…”

Section: State Of the Artmentioning

confidence: 99%

Community Sports Facility Ant Colony Algorithm Collocation under the Environment of National Fitness

Yong-mei

2022

Wireless Communications and Mobile Computing

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The construction of sports facilities is gradually gaining public attention in the national fitness environment. Community sports facilities make a great contribution in making up for the shortage of urban gymnasiums, but there are many similarities in the existing community sports facilities, and the collocation is only to meet the basic needs. Based on this, this paper investigates the study of an ant colony algorithm for the collocation of community sports facilities in a national fitness environment. Based on a brief analysis of the construction of community sports facilities and the study of ant colony algorithms, a model of community sports facility matching is constructed and an ant colony algorithm is introduced to optimize the design of the scheme. The multiobjective function model is combined with the demand for community sports settings collocation, and the optimal solution set for different planning objectives is proposed in the optimal design of sports facility collocation. The objective function is combined with the ant colony algorithm to continuously update the objective function value and obtain the optimal solution after stabilization, providing a more excellent configuration for sports facility collocation. The simulation results show that the performance of this algorithm is more stable than that of traditional algorithms, and it can provide more diverse solutions for sports facility matching with greater stability.

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Section: State Of the Artmentioning

confidence: 99%

Community Sports Facility Ant Colony Algorithm Collocation under the Environment of National Fitness

Yong-mei

2022

Wireless Communications and Mobile Computing

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“…By exploring the design space created by MOACO algorithms with the new methodology of creating multi-objective optimizers, new MOACO algorithms were automatically generated that outperformed all the earlier proposed ACO algorithms in this field [34]. In more recent research, Falcn-Cardona and Coello Coello further extended the framework presented by Sttzle et al to present a novel approach for multi-objective problems with ACO algorithm variant called iMOACO-RR [35].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Evolution of Ant Colony Optimization Algorithm -- A Brief Literature Review

Akhtar

2019

Preprint

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Ant Colony Optimzation (ACO) is a metaheuristic proposed by Marco Dorigo in 1991 based on behavior of biological ants. Pheromone laying and selection of shortest route with the help of pheromone inspired development of first ACO algorithm. Since, presentation of first such algorithm, many researchers have worked and published their research in this field. Though initial results were not so promising but recent developments have made this metaheuristic a significant algorithm in Swarm Intelligence. This research presents a brief overview of recent developments carried out in ACO algorithms in terms of both applications and algorithmic developments. For application developments, multi-objective optimization, continuous optimization and time-varying NP-hard problems have been presented. While to review articles based on algorithmic development, hybridization and parallel architectures have been investigated.

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“…Exploring the resulting design space of MOACO algorithms through a novel methodology for generating automatically multi-objective optimizers, they could generate new MOACO algorithms that clearly outperformed all previously proposed ACO algorithms for multi-objective optimization [121]. Such framework may also be further extended to consider more recent ACO approaches to many-objective problems such as those proposed by Falcón-Cardona and Coello Coello [77].…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

An Introduction to Ant Colony Optimization

Dorigo

Socha

2018

Handbook of Approximation Algorithms and Metaheuristics, Second Edition

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Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic solution construction procedures that build candidate solutions for the problem instance under concern by exploiting (artificial) pheromone information that is adapted based on the ants' search experience and possibly available heuristic information. Since the proposal of Ant System, the first ACO algorithm, many significant research results have been obtained. These contributions focused on the development of high performing algorithmic variants, the development of a generic algorithmic framework for ACO algorithm, successful applications of ACO algorithms to a wide range of computationally hard problems, and the theoretical understanding of important properties of ACO algorithms. This chapter reviews these developments and gives an overview of recent research trends in ACO.

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A new indicator-based many-objective ant colony optimizer for continuous search spaces

Cited by 33 publications

References 40 publications

Community Sports Facility Ant Colony Algorithm Collocation under the Environment of National Fitness

Community Sports Facility Ant Colony Algorithm Collocation under the Environment of National Fitness

Evolution of Ant Colony Optimization Algorithm -- A Brief Literature Review

An Introduction to Ant Colony Optimization

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