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Chaotic Immune evolutionary algorithm (CIEA) is proposed on the shortage of current algorithms training artificial neural network, biological immune mechanism and the characteristics of evolution. A novel artificial neural network based on chaotic immune evolutionary algorithm (CIEANN) is presented in this paper. The algorithm has the merits of chaos, immunity and evolutionary algorithm. It can ensure...
In complicated multi-objective optimization, it often happens that points in part region of Pareto front are easy to get, but in others are difficult. To obtain evenly distributed Pareto optimal solution, we construct dynamical crossover and mutation probability which can self-adaptively adjust the number of individuals engaged in crossover and mutation, combine with the fitness function constructed...
Evolutionary algorithms (EAs) have wide applications in practice and many advantages over traditional methods in solving nonlinear and complex optimal problems. In this paper, we propose a novel clustering technique, in which the infeasible solutions are employed to divide the feasible solutions into several clusters. There is no more one infeasible individual in each cluster. A novel evolutionary...
So far there are a number of evolutionary algorithms (EAs) applied in solving multi-objective optimization problems (MOPs), but it is very hard to evaluate the performance of a multi-objective optimization evolutionary algorithm (MOEA) especially to equably evaluate the Pareto Front (PF) when the dimension of the objective space is greater than 2. This paper has made a corresponding analysis on the...
A joint strategy of stock provision and age-based preventive maintenance is considered. The joint strategy takes the form (T, s, S), where T is scheduled preventive replacement age, s is stock reorder point and S is maximum inventory level. The optimal values of the decision variables are obtained by minimizing the total cost of maintenance and inventory. To accomplish the optimizing task, a general...
In this paper, Optimization of multi-resource allocation and leveling is studied. A mathematical model of multi-resource allocation and leveling problem for bi-objective and multi-restricted condition is set up. A self-adaptive ant colony algorithm for the problem is presented. According to the topological relations of network graph, we develop the method of serial scheduling generation scheme of...
A new hybrid evolution genetic algorithm for constrained optimization is proposed in this paper. This algorithm is based on feasible and infeasible population and mixed crossover with mutation operations. It introduces temporary feasible and infeasible population and maintains a fixed scale of the feasible and infeasible population in each generation. Through the genetic repair strategy and definitions...
Capacitated vehicle routing problem(CVRP) is an important combinatorial optimization problem which has received considerable attention in the last decades. The Ant Colony system (ACS) is a metaheuristic which is inspired by the trail following behavior of real ant colonies. This paper proposes a two-stage hybrid ACS algorithm for CVRP. The algorithm first minimizes the number of vehicles using ACS...
Genetic algorithm and particle swarm optimization both belong to the evolutionary algorithms; they have much in common, but also have some differences. The paper set out from optimizing many resources, discussed the method of utilizing GA and PSO in detail, in order to equilibrium and optimize the problem of scheduling resources which are limited separately. Through analysis of comparative experiment,...
Genetic algorithm and ant colony algorithm are the methods for solving winner determination problem in combinatorial auctions. Because of their mutual compensability: ant colony algorithm can make up for the shortage of feedback information and the slowness of solving solution in genetic algorithm, while genetic algorithm is able to enhance the speed of ant colony algorithm in solving solution and...
According to the characteristics of the multi-resource, multi-depot and multi-objective emergency scheduling problem, this paper establishes a multi-objective mathematical model with the goal of the shortest time and the smallest cost. We propose an algorithm for multi-objective fuzzy decision using the fuzzy ideal point method, which transforms the multi-objective decision into the single-objective...
This paper presents neurodynamic method for solving a class of nonlinear fractional optimization problems with bounds constraints. The neurodynamic model have the following two properties. First, it is demonstrated that the set of optima to the problems coincides with the set of equilibria of the neurodynamic models which means the proposed model is complete then. Second, it is also shown that the...
The incorporation of prior knowledge into SVMs for classification is the key element that allows increasing the performance to many applications. Wu proposed weighted margin support vector machine (WMSVM), the scalability aspect of the approach to handle large data sets still needs much of exploration. In this paper, we describe a generalization of weighted margin multi-class core vector machine (WMMCVM)...
Use the concept of stochastic flow network to describe transportation optimization problems upon logistic system. Select transportation reliability, transportation cost and time as objectives, build model of network flow optimization in logistic network. Take MPs as unit to build model, so model??s complexity is decreased. Use NSGA-II algorithm to solve built model and obtain the Pareto solutions...
This paper introduces a new hybrid rule-based language, which integrates seamlessly with object-oriented language. This language has two distinct properties: separates business rules as a single module from the main program to facilitate the system modification and the maintenance; and allows the close interaction between the rule context and the main program context. Using this language, we can express...
Fuzzy models have capability for solving problem in different application such as pattern recognition, prediction and control. Nevertheless, it has to be emphasized that the identification of a fuzzy model is complex task with many local minima. Cartesian genetic programming provides a way to solve such complex optimization problem. In this paper, fuzzy model is in form of network. Cartesian genetic...
This paper proposes an improved multi-swarm cooperative particle swarm optimizer with center communication (MCPSO-CC) based on our previous proposed MCPSO algorithm, which enhances the particles based on the experience of master swarm and slave swarms. In our original MCPSO, there is no information sharing among slave swarms except that the information of the best performing particle is broadcasted...
Emotion recognition based on physiological signals has a significant future of research and applications. However, in the process of emotion recognition, it is difficult to obtain the most significant feature combinations. Dual-Structure Particle Swarm Optimization (DSPSO) is applied to select emotion features of physiological signals so as to improve the recognition rates in this paper. K-Nearest...
An improved particle swarm optimization algorithm was proposed to fit multi-peaks searching; this algorithm was combined with number-theoretical method. In this algorithm, Number-theoretic net was used to initialize the particles' position, and for the purpose of multi-peak searching, the evolution equation was modified. The result of PSO is fined by a method named creeping algorithm for improving...
The particle swarm optimization (PSO) is one of the best efficient algorithms. It has obtained more and more attention and has been applied in many fields, such as machine design and circuit design. But it also has some disadvantages, such as prematurely and difficultly to convergence. To improvement the performance of PSO, particle reliving strategy is proposed. With this strategy, a criterion is...
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