A multi-agent memetic algorithm approach for distributed object allocation

Pendharkar, Parag C.

doi:10.1016/j.jocs.2011.08.002

Cited by 10 publications

(3 citation statements)

References 31 publications

(45 reference statements)

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“…The study in [22] shows similar conclusions, although Danoy et al further point out that LCGA is more scalable than CCGA. Besides the LCGA, a multi-agent memetic algorithm named MA 2 is developed in [98], in which each agent in the multi-agent system is a subpopulation of a memetic algorithm (GA with local search). The algorithm has also shown its powerfulness in tackling high-dimensional optimization problems.…”

Section: Multi-agent Modelmentioning

confidence: 99%

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Gong

Chen

Zhan

et al. 2015

Applied Soft Computing

333

135

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a b s t r a c tThe increasing complexity of real-world optimization problems raises new challenges to evolutionary computation. Responding to these challenges, distributed evolutionary computation has received considerable attention over the past decade. This article provides a comprehensive survey of the state-of-the-art distributed evolutionary algorithms and models, which have been classified into two groups according to their task division mechanism. Population-distributed models are presented with master-slave, island, cellular, hierarchical, and pool architectures, which parallelize an evolution task at population, individual, or operation levels. Dimension-distributed models include coevolution and multi-agent models, which focus on dimension reduction. Insights into the models, such as synchronization, homogeneity, communication, topology, speedup, advantages and disadvantages are also presented and discussed. The study of these models helps guide future development of different and/or improved algorithms. Also highlighted are recent hotspots in this area, including the cloud and MapReduce-based implementations, GPU and CUDA-based implementations, distributed evolutionary multiobjective optimization, and real-world applications. Further, a number of future research directions have been discussed, with a conclusion that the development of distributed evolutionary computation will continue to flourish.

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Section: Multi-agent Modelmentioning

confidence: 99%

Distributed evolutionary algorithms and their models: A survey of the state-of-the-art

Gong

Chen

Zhan

et al. 2015

Applied Soft Computing

333

135

View full text Add to dashboard Cite

show abstract

“…Based on the simulation of the process of cultural evolution, it is a marriage between a population-based global search and the heuristic local search made by each of the individuals. In many cases, MAs have been shown to be capable of finding (near-) optimum solutions [4][5][6]. Nevertheless, even MAs may still fall victim to either slow or premature convergence.…”

Section: Introductionmentioning

confidence: 99%

A Constraints Scattered Memetic Algorithm for Constrained Optimization Problem

Zhu

Xú²,

Wang³

et al. 2012

JCP

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In this paper, a constraints scattered memetic algorithm (CSMA), which integrates a novel constraints scattered genetic algorithm (CSGA) and the traditional interior point method, is proposed for solving constrained optimization problems. In CSGA, a constraint scattering operation, a sub-population crossover method and a new population performance evaluation mechanism are employed. The complete constraints of a problem are divided into several sub-populations and all these subpopulations are crossed after their respective evolution process. In this way, the difficulty of obtaining feasible individuals in many strong constrained conditions is well overcome. And according to the newly defined population performance index, individuals with larger population diversity are chosen for further local search. These new mechanisms are combined in CSGA and interior point method is further employed as a local search operator for exploitation. Experiments and comparisons over a set of standard test functions show that population with better performance can be generated during the iteration of CSGA and the proposed CSMA has a better solution precision at less computation cost than most of the other algorithms reported in literature.

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“…In keeping with our objectives for JoCS, this issue covers a broad range of computational science [1][2][3][4][5][6][7][8][9]. The featured papers include discussions of novel topics such as stochastic simulations of intra-cellular transport, new approaches to coupled systems in computational fluid dynamics, the emergence of geographical patters of electoral preferences and the optimisation of climate model parameters.…”

mentioning

confidence: 99%