Inspired by physical force, Artificial Physics Optimization (APO,)algorithm is presented based on Physicomimetics framework. Driven by virtualforce, a population of sample individuals searches a global optimum in thep...
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A reactive power optimization is a multi-modal, mixed-variable, multi-constraint and nonlinear planning problem. In the last decades, many computational intelligence-based techniques have been proposed for reactive po...
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A reactive power optimization is a multi-modal, mixed-variable, multi-constraint and nonlinear planning problem. In the last decades, many computational intelligence-based techniques have been proposed for reactive power optimization problem, such as genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), Tabu search. Recently, a new swarm intelligent algorithm, social cognitive optimization algorithm (SCOA), is proposed by simulating the human competition process. In this paper, it is introduced to solve reactive power problem. Two famous examples: IEEE-57bus and IEEE-118bus system are used to test, simulation results show SCOA is effective.
Estimation of Distribution Algorithms (EDAs) are implemented mainly by the three steps: selecting the promising subset from the current population, modeling the distribution of the selected population and sampling fro...
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To answer the problem of ambiguous design levels for large-scale distributed simulationsystems, this paper proposes a hierarchical system model based on the quotient space theory. This model consists of a system glob...
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To show the validity of swarm robots modeling and control properties influenced by resulting algorithmic parameter settings, particle swarm optimization (PSO) is extended to be tools for applying to swarm robotic sear...
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The use of evolutionary algorithms to solve unconstraint multi-objective problems (MOPs) has attracted much attention recently. However, research on constraint multi-objective algorithms is relatively less. The author...
The use of evolutionary algorithms to solve unconstraint multi-objective problems (MOPs) has attracted much attention recently. However, research on constraint multi-objective algorithms is relatively less. The authors introduce a novel evolutionary paradigm of artificial physics optimization (APO) into constraint multi-objective optimization domain and modify the original mass function and virtual force rules in order to fit constraint multi-objective optimization problems. Moreover the authors present a method of virtual force decreasing to improve the efficiency. Finally, simulation tests show that the algorithm is effective.
Estimation of Distribution Algorithms (EDAs) is a novel evolutionary algorithm originated from Genetic Algorithms. The probability distribution model of promising population is estimated iteratively in EDAs, and the n...
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Estimation of Distribution Algorithms (EDAs) is a novel evolutionary algorithm originated from Genetic Algorithms. The probability distribution model of promising population is estimated iteratively in EDAs, and the new generation is sampled from the estimated model. An EDA with Gumbel copula is proposed in this paper. In order to estimating the joint, the empirical margins of each variable are estimated separately, and the relationship of variables is presented by Gumbel copula. On the ground of copula theory, the joint is the composite function of the copula and the margins. This algorithm simplifies the operator to estimating the multivariate distribution. The experimental results show that the proposed algorithm is equivalent to some conventional continuous EDAs in performance.
For node controlling in target tracking of wireless multimedia sensor networks whose sensors are deployed randomly, the authors build a rotatable sense model for sense node and put forward a node controlling algorithm...
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To explore the relation between topologic characteristics of dynamic network and performance of the particle swarm optimization (PSO) algorithm, the population of PSO is viewed as a network where each particle is repr...
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To explore the relation between topologic characteristics of dynamic network and performance of the particle swarm optimization (PSO) algorithm, the population of PSO is viewed as a network where each particle is represented as a node and the network structure changes dynamically as the fitness of particles varies. Moreover, in this paper, the structural changes involve adding and removing the links but the network size remains the same. Then, two kinds of simulations are conducted. The results from one kind focusing on PSO show that the dynamic network is capable of balancing exploration and exploitation so that the performance of PSO can be improved as long as the weight θ is selected properly. In addition, the results from other kind concerning on topologic characteristics of dynamical network indicate the impact of network structure on algorithm behavior and the law of network evolution.
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