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Focus on reverse logistics location of remanufacturing factory, a mixed integer programming model is constructed on the basis of minimum cost. Then particle swarm optimization (PSO) algorithm is introduced in the model. Discrete PSO is used to solve reverse logistics location of remanufacturing factory, and the distribution of goods is solved by traditional PSO algorithm. The optimal solution of the...
Automated Complementary Metal Oxide Semiconductor (CMOS) logic circuit design leads to the reduction in costs associated with manpower and manufacturing time. Conventional methods use repetitive manual testing guided by Logical Effort (LE). LE provides an easy way to compare and select circuit topologies, choose the best number of stages for path and estimate path delay. In this paper, we propose...
In this paper, we propose a novel algorithm to enhance the noisy speech in the framework of dual-channel speech enhancement. The new method is a hybrid optimization algorithm, which employs the combination of the conventional θ-PSO and the shuffled sub-swarms particle optimization (SSPSO) technique. It is known that the θ-PSO algorithm has better optimization performance than standard PSO algorithm,...
Recurring Multistage Evolutionary Algorithm is a novel evolutionary approach that is based on repeating conventional, explorative and exploitative genetic operations in order to perform better optimization with improved robustness against local optima. This work compares the performance of RMEA with that of classical evolutionary algorithm, differential evolution and particle swarm optimization on...
This paper introduces a novel adaptation scheme of mutation step size for the Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms on complex multimodal benchmark problems. The Artificial Bee Colony (ABC) is a swarm based optimization algorithm mimicking the intelligent food foraging behavior of honey bees. The proposed...
The problem in parameter selection of least squares support vector machine (LS-SVM) restricts the development of LS-SVM, In order to choose the optimal parameters of LS-SVM automatically, we proposed an improved particle swarm optimization (PSO) algorithm which can not only increase the convergent speed but also improve the overall searching ability of the algorithm. The improved PSO algorithm can...
In this paper, a novel method for robotic belt grinding based on support vector machine and particle swarm optimization algorithm is presented. Firstly, the dynamic model of the robotic belt grinding process is built using support vector machine method. This is the basis of our work because the dynamic model shows the relation between the removal and control parameters (contact force and robot's speed)...
In this paper, a new swarm intelligence algorithm called SIA for global optimization is proposed. Each individual in population is firstly projected onto the boundary of the search space in order to extend the region of the global search. Secondly, a probability of generating new individual is computed based on the function values of the individual, its corresponding boundary point and the best individual...
To minimize energy consumption in the Wireless Sensor Networks (WSNs), we propose a decentralized sensor coordination optimization scheme for Mobile Multi-Target Tracking (MMTT) in WSNs. Our scheme partitions the available sensor-nodes into clusters using the maximum-entropy based clustering criteria. For each tracked target, a number of neighboring clusters are activated based on their Hausdorff...
Particle swarm optimization (PSO) is simple and efficient, but there is serious premature convergence for solving constrained optimization problem. In order to control premature convergence, this paper proposed dynamic neighborhood hybrid particle swarm optimization (DNH_PSO), which firstly uses the dynamic neighborhood strategy that based on the random topology and the von Neumann topology to improve...
In this paper an eminent approach based on the paradigms of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, fuzzy logic is applied for planning and then scheduling is optimized using evolutionary computing algorithms such as Genetic Algorithm (GA), and Particle...
Quantum Evolutionary Algorithm (QEA) is an optimization algorithm based on the concept of quantum computing and Particle Swarm Optimization (PSO) algorithm is a population based intelligent search technique. Both these techniques have good performance to solve optimization problems. PSEQEA combines the PSO with QEA to improve the performance of QEA and it can solve single objective optimization problem...
In this paper, a particle swarm optimization algorithm with Gaussian mutations, denoted by GPSO, is proposed to solve constrained optimization problems. Two Gaussian mutation operators are employed to search the promising regions for better solutions. One operator is for the region between the personal best position and the global best one. The other operator is for the region around the global best...
In this paper, a fuzzy facility location model with Value at Risk (VaR) is proposed, which is a two-stage fuzzy zero-one integer programming. Since the fuzzy parameters of the location problem are continuous fuzzy variables with an infinite support, the computation of VaR is inherently an infinite-dimensional optimization problem, which can not be solved analytically. In order to solve the model,...
In this article, a new algorithm which is obtained by hybridizing cellular learning automata and artificial fish swarm algorithm (AFSA) is proposed for optimization in continuous and static environments. In the proposed algorithm, each dimension of search space is assigned to one cell of cellular learning automata and in each cell a swarm of artificial fishes are located which have the optimization...
Data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA)...
This paper describes the optimization of Mel Frequency Cepstral Coefficients (MFCC) parameters using Discrete Mutative Particle Swarm Optimization (DMPSO) for diagnosis of hypothyroidism in infants. The MFCC was used to extract the feature set from infant cry signals. The features were then classified using Multi-Layer Perceptron (MLP). The DMPSO variants optimize the number of filter banks and number...
A projective point matching algorithm based on modified particle swarm optimization is presented. In the paper, the point matching problem turns into an optimization with two series of parameters, projective transform parameters and correspondent mapping parameters. Firstly, a modified particle swarm optimization (PSO) is introduced and a new rule searching for correspondences, closer point matching...
Delay tolerant networks (DTNs) are a class of emerging networks that experience frequent and long-duration partitions. Multicast supports the distribution of data to a group of users, a service needed for many potential DTN applications, due to the unique characteristic of frequent partitioning in DTNs, multicasting in DTNs is a considerably different and challenging problem. In this paper, The mathematics...
In this paper, we propose a novel adaptive fuzzy weight parameter PSO Algorithm (FPSO). In the improved algorithm, the inertia weight reserves its decreasing property after fuzzy treatment, and the position is controlled by fuzzy parameter. Simulations have been done to illustrate that the improved algorithm can regulate global search and local search, and has better search accuracy than the basic...
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