Multi-objective Optimization Using Co-evolutionary Multi-agent System with Host-Parasite Mechanism

Dreżewski, Rafał; Siwik, Leszek

doi:10.1007/11758532_113

Cited by 23 publications

(13 citation statements)

References 7 publications

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“…A trust value measures the suitability of the services for solving a particular problem. 27 Many components of this approach are similar to those of SCoEMAS, and its performance compared with existing well-known metaheuristics is also close to that of SCoEMAS. A particularly selected service provides a positive outcome when the created offspring via that service can survive to the next generation; otherwise, the service affords a negative outcome.…”

Section: Multiagent Systems For Multiobjective Optimizationmentioning

confidence: 63%

“…Drezewsky and Siwik introduced another work on MAS for MOO that is based on inspirations from host-parasite mechanisms, and the corresponding method is named as HPSoEMAS. 27 Many components of this approach are similar to those of SCoEMAS, and its performance compared with existing well-known metaheuristics is also close to that of SCoEMAS.…”

Section: Multiagent Systems For Multiobjective Optimizationmentioning

confidence: 63%

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A multimetric and multideme multiagent system for multiobjective optimization

Tamouk

Acan

2018

Computational Intelligence

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This article proposes a multiagent system consisting of a number of multiobjective metaheuristic agents (namely, multiobjective genetic algorithm, strength Pareto evolutionary algorithm, differential evolution, simulated annealing, and particle swarm optimization) working toward to extract optimal or very close-to-optimal Pareto fronts using multiple performance metrics in a sessionwise manner. At the beginning of each session, the main population is divided into a number of subpopulations, and each of them is assigned to a particular agent. The system runs in consecutive sessions such that, at the beginning of a session, agents start running after being assigned with a subpopulation and return the optimized subpopulations together with the corresponding set of nondominated solutions at the end of the session. There are 3 multiobjective assessment metrics in use, and a different metric is considered for each session to measure the success of each metaheuristic agent. The evaluation of individual agents using a particular assessment metric is used in 2 ways: first, the number of fitness evaluations for each agent is adjusted based on their performance; second, the subpopulation improved by an individual agent might be rejected on the basis of its evaluation score. At the end of each session, individual subpopulations are merged to get the updated main population, whereas individual sets of nondominated solutions are combined to form the global Pareto front. In addition to the individual multiobjective metaheuristic agents, the system also contains a number of coordination and synchronization agents that run the whole system toward its objectives. 1122

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Section: Multiagent Systems For Multiobjective Optimizationmentioning

confidence: 63%

Section: Multiagent Systems For Multiobjective Optimizationmentioning

confidence: 63%

A multimetric and multideme multiagent system for multiobjective optimization

Tamouk

Acan

2018

Computational Intelligence

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“…In this work the model of co-evolutionary interactions between species and sexes was introduced-basically it allowed for constructing the agent-based evolutionary algorithms utilizing many co-evolving species and sexes. Co-evolutionary multi-agent systems (CoEMAS) were applied with success to multi-modal optimization (for example see [41]) and multi-objective optimization (for example see [45][46][47]), in which case it allowed for constructing novel and very effective mechanisms for maintaining the population diversity [48]. The CoEMAS model was then generalized and the bio-inspired multi-agent system for simulation and computations was proposed in [49].…”

Section: Agent-based Co-evolutionary Algorithmsmentioning

confidence: 99%

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Dreżewski

Doroz

2017

Symmetry

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Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach-the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

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“…The general model of co-evolutionary multi-agent system (CoEMAS) [6] introduces additionally the notions of species, sexes, and interactions between them. CoEMAS allows modeling and simulation of different co-evolutionary interactions, which can serve as the basis for constructing the techniques of maintaining population diversity and improving adaptive capabilities of such systems (for example see [7]). …”

Section: Introductionmentioning

confidence: 99%

Techniques for Maintaining Population Diversity in Classical and Agent-Based Multi-objective Evolutionary Algorithms

Dreżewski

Siwik

2007

Computational Science – ICCS 2007

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Abstract. The loss of population diversity is one of the main problems in some applications of evolutionary algorithms. In order to maintain useful population diversity some special techniques must be used, like niching or co-evolutionary mechanisms. In this paper the mechanisms for maintaining population diversity in agent-based multi-objective (co-)evolutionary algorithms are proposed. The presentation of techniques is accompanied by the results of experiments and comparisons to "classical" evolutionary multi-objective algorithms.

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Multi-objective Optimization Using Co-evolutionary Multi-agent System with Host-Parasite Mechanism

Cited by 23 publications

References 7 publications

A multimetric and multideme multiagent system for multiobjective optimization

A multimetric and multideme multiagent system for multiobjective optimization

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Techniques for Maintaining Population Diversity in Classical and Agent-Based Multi-objective Evolutionary Algorithms

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