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Artificial Physics Optimization (APO) Algorithm is a novel population-based stochastic algorithm for solving the unconstrained global problems. This paper first presents a simple mechanism to handle constrained optimization problems with APO. A feasibility-based rule is employed, because this rule can guide the swarm quickly to the feasible region and need not additional penalty parameters. The mass...
This paper proposes an adaptive repulsive particle swarm optimization (ARPSO) for minimizing the makespan and maximum lateness in the permutation flowshop scheduling problem (PFSP). ARPSO develops a heuristic rule called the smallest distance value (SDV) to present the discrete job permutation for the PFSP. And ARPSO uses several novel evolutionary strategies to avoid premature convergence and improve...
Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm. A quantum-behaved particle swarm optimization (QPSO) is also proposed by combining the classical PSO philosophy and quantum mechanics. These algorithms have been very successful in solving the global continuous optimization, but their applications to combinatorial optimization have been rather limited. Estimation...
Particle swarm optimization can be viewed as a distributed agent model, but many agent computing characteristics are still uncovered. This paper combines multiagent system and genetic particle swarm optimization (GPSO) and proposes a multiagent-based GPSO approach (MAGPSO), for combinatorial optimization problems. In MAGPSO, an agent represents a particle to GPSO and a candidate solution to the optimization...
The philosophy behind the original particle swarm optimization (PSO) is to learn from individual's own experience and the best individual experience in the whole swarm. Estimation of distribution algorithms (EDAs) generate new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. In this paper, a discrete estimation of...
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