Hybrid Swarm and Agent-Based Evolutionary Optimization

Placzkiewicz, Leszek; Sendera, Marcin; Szlachta, Adam; Paciorek, Mateusz; Byrski, Aleksander; Kisiel-Dorohinicki, Marek; Godzik, Mateusz

doi:10.1007/978-3-319-93701-4_7

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…[29,30]. Currently, two hybridization of EMAS are researched, namely the one with Differential Evolution (in progress, not published yet) and with Particle Swarm Optimization (preliminary results already published in [26]).…”

Section: Resultsmentioning

confidence: 99%

Fine Tuning of Agent-Based Evolutionary Computing

Mizera

Nowotarski

Byrski

et al. 2018

Journal of Artificial Intelligence and Soft Computing Research

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Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

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Section: Resultsmentioning

confidence: 99%

Fine Tuning of Agent-Based Evolutionary Computing

Mizera

Nowotarski

Byrski

et al. 2018

Journal of Artificial Intelligence and Soft Computing Research

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“…We would rather try to pave the way for such a hybridization for other algorithms by considering EA and ACO only in the beginning. Imagine that it would be quite interesting to add, for example, an Estimation of Distribution Algorithm 7 (considering the sampling distributions as well as the individuals themselves) or Particle Swarm Optimization 8,9 (adding information about the speed of the particles). Moreover, other types of Talbi's hybrids may be easily considered; for example, one can embed the metaheuristic (as ACO inside EA, for example—according to LTH of Talbi's classification) when applying the proposed translation mechanism.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization–evolutionary hybrid optimization with translation of problem representation

Polnik

Stobiecki

Byrski

et al. 2021

Computational Intelligence

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Different hybrid optimization metaheuristics (see the works of Talbi for classification) either assume the embedding of one algorithm (usually a metaheuristic) in another (for instance, a local search inside an evolutionary algorithm-a memetic algorithm) or creating a chain of algorithms. In this paper, such a chain combination of two algorithms (namely, the Ant Colony Optimization and Evolutionary Algorithm) is presented.However, because of the intrinsic differences between the two algorithms (a vector of labels and a pheromone table when solving the traveling salesman problem, for example), several dedicated algorithms for translating the solutions between these two representations of the problem are proposed. The hybrid algorithm constructed with the application of the translation methods turns out to be significantly better in solving the TSP compared to non-hybrid versions (relevant experimental results are presented and discussed). This paves the way for new possibilities of constructing hybrid metaheuristics by putting together completely different ones (using different representations); the impact of the presented

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“…There is no central control, it can all be easily paralleled, which reduces the computation time. After careful analysis [4,16], it has become a solid base for attempts to combine with other ideas, giving some interesting hybrid algorithms ( [13] and [9]). However, when connecting, one may encounter the problem of redistributing the energy of agents making their own decisions to use algorithms that do not use energy ( [10]).…”

Section: Introductionmentioning

confidence: 99%

Energy redistribution in autonomous hybridization of agent-based computing

Godzik

2021

csci

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Evolutionary multi-agent systems (EMAS) are very good at dealing with difficult, multi-dimensional problems. Currently, research is underway to improve this algorithm, giving even more freedom to agents not only in solving the problem but also in making decisions on the behavior of the algorithm. One way is to hybridize this algorithm with other existing algorithms creating Hybrid Evolutionary Multi Agent-System (HEMAS). Unfortunately, such connections generate problems in the form of an unbalanced energy level of agents who have made the decision to use such an improvement. One of the solutions is the mechanism of redistributing the agents' energy in the form of an operator. The article presents several proposals of redistribution operators along with numerous experimental results.

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Hybrid Swarm and Agent-Based Evolutionary Optimization

Cited by 5 publications

References 17 publications

Fine Tuning of Agent-Based Evolutionary Computing

Fine Tuning of Agent-Based Evolutionary Computing

Ant colony optimization–evolutionary hybrid optimization with translation of problem representation

Energy redistribution in autonomous hybridization of agent-based computing

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