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International Conference on Artificial Intelligence and Computational Intelligence

作者： Liu, Jian-guo Congqing Bussiness & Technol Univ Coll Applicat Technol Chongqing Peoples R China

ISBN: (纸本)9780769538167

Enlightened by the knowledge of ecological environment and population competition, we proposed a competitive coevolutionary genetic algorithm (CCGA) based on ecological population competition mode for multiobjective optimization problems. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set, enforcing a more uniform distribution of such vectors along the Pareto front. The proposed approach was validated using typical test function taken from the specialized literature. Our comparative study showed that the proposed approach is competitive with respect three other algorithms that are representative of the state-of-the-art in evolutionary multiobjective optimization.

关键词： competitive coevolutionary genetic algorithm Pareto optimal point multiobjective optimization problems

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coevolutionary Optimization algorithm: With Ecological Competition Model

Coevolutionary Optimization Algorithm: With Ecological Compe...

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3rd International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011)

作者： Liu, Jianguo Wu, Weiping Congqing Business & Technol Univ Chongqing 400067 Peoples R China

ISBN: (纸本)9783642242816

Premature convergence and low converging speed are the distinct weaknesses of the genetic algorithms, a new algorithm called ECCA (ecological competition coevolutionary algorithm) is proposed for multiobjective optimization problems, in which the competition is considered to be in important position. In the algorithms, each objective corresponds to a population. At each generation, these populations compete among themselves. An ecological population density competition equation is used for reference to describe the relation between multiple objectives and to direct the adjustment over the relation at individual and population levels. The proposed approach store the Pareto optimal point obtained along the evolutionary process into external set, enforcing a more uniform distribution of such vectors along the Pareto front. The experiment results show the high efficiency of the improved genetic algorithms based on this model in solving premature convergence and accelerating the convergence.

关键词： competitive coevolutionary genetic algorithm Pareto optimal point multiobjective optimization problems

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A competitive Predator-Prey approach to enhance surveillance by UAV swarms

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APPLIED SOFT COMPUTING 2021年 111卷 107701-107701页

作者： Stolfi, Daniel H. Brust, Matthias R. Danoy, Gregoire Bouvry, Pascal Univ Luxembourg Interdisciplinary Ctr Secur Reliabil & Trust SnT Luxembourg Luxembourg Univ Luxembourg FSTM DCS Luxembourg Luxembourg

In this paper we present the competitive optimisation of a swarm of Unmanned Aerial Vehicles (UAV) protecting a restricted area from a number of intruders following a Predator-Prey approach. We propose a competitive coevolutionary genetic algorithm (CompCGA) which optimises the parameters of the UAVs (i.e. predators) to maximise the detection of intruders, while the parameters of the intruders (i.e. preys) are optimised to maximise their intrusion success rate. Having chosen the CACOC (Chaotic Ant Colony Optimisation for Coverage) as the base mobility model for the UAVs, we propose an improved new version, where its behaviour is modified by identifying and optimising new parameters to improve the overall success rate when detecting intruders. Six case studies have been optimised using simulations by performing 30 independent runs (180 in total) of our CompCGA. Finally, we conducted a series of master tournaments (1,800,000 evaluations) using the best specimens obtained from each run and case study to test the robustness of our proposed approach against unexpected intruders. Our surveillance system improved the average percentage of intruders detected with respect to CACOC by a maximum of 126%. More than 90% of intruders were detected on average when using a swarm of 16 UAVs while CACOC's detection rates are always under 80% in all cases. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Swarm robotics Computer simulation Mobility model Unmanned aerial vehicle competitive coevolutionary genetic algorithm

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