Cecilia Di Chio
ABSTRACT
Recent work has introduced a simulation model of ecological processes in terms of a very simple Particle Swarm algorithm. This abstract model produced qualitatively realistic behaviours, but do these results hold up in a model constrained by more plausible biological assumptions? The objective of this paper is to answer this question.
- M.A. Bedau, Can Unrealistic Computer Models Illuminate Theoretical Biology?, GECCO1999 -- Proceedings of the 1999 Genetic and Evolutionary Computation Conference Workshop Program, 1999Google Scholar
- D.L. DeAngelis and W.M. Mooij, Individual-Based Modeling of Ecological and Evolutionary Processes, Annual Reviews in Ecology, Evolution and Systematics, 2005Google Scholar
- C. Di Chio, R. Poli and P. Di Chio, Extending the Particle Swarm Algorithm to Model Animal Foraging Behaviour, University of Essex, Technical Report, 2006.Google Scholar
- C. Di Chio, R. Poli and P. Di Chio, Modelling Group-Foraging Behaviour with Particle Swarms, PPSN2006 -- Ninth International Conference on Parallel Problem Solving from Nature, 2006. Google ScholarDigital Library
- E.A. Di Paolo, J. Noble and S. Bullock, Simulation Models as Opaque Thought Experiments, Artificial Life VII -- Proceedings of the Seventh International Conference on the Simulation and Synthesis of Living Systems, 2000.Google Scholar
- J. Kennedy and R.C. Eberhart, Swarm Intelligence, Morgan Kaufmann Publishers, 2001. Google ScholarDigital Library
- J. Krause and G.D. Ruxton, Living in Groups, Oxford University Press, 2002.Google Scholar
- D. MacFarland, Animal behaviour, Longman, 1999.Google Scholar
- J.K. Parrish and W.M. Hamner, Animal Groups in Three Dimensions, Cambridge University Press, 1997.Google ScholarCross Ref
- J.W. Pitchford, A. James and J. Brindley, Optimal Foraging in Patchy Turbulent Environments, Marine Ecology Progress Series, 2003Google ScholarCross Ref
Index Terms
- EcoPS: a particle swarm algorithm to model group-foraging
Recommendations
A Modified Quantum-Behaved Particle Swarm Optimization
ICCS '07: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007Based on the previously introduced Quantum-behaved Particle Swarm Optimization (QPSO), a revised QPSO with Gaussian disturbance on the mean best position of the swarm is proposed. The reason for the introduction of this novel method is that the ...
PSO+: A new particle swarm optimization algorithm for constrained problems
AbstractThe Particle Swarm Optimization algorithm is a metaheuristic based on populations of individuals in which solution candidates evolve through simulation of a simplified model of social adaptation. By aggregating robustness, efficiency ...
Highlights- The algorithm ensures there will always be particles fully respecting constraints.
Multivector particle swarm optimization algorithm
AbstractThis paper proposes an improved meta-heuristic algorithm called multivector particle swarm optimization (MVPSO) for solving single-objective optimization problems. MVPSO improves particle swarm optimization (PSO) algorithm by creating more ...
Comments