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Many search strategies have been exploited in implementing feature selection, in an effort to identify smaller and better subsets. Such work typically involves the use of heuristics in one form or another. In this paper two novel methods are presented by applying harmony search to feature selection. In particular, it demonstrates the potential of utilising this search mechanism in combination with...
Dynamic optimization is one of the important research area in the intelligence computation field. For a decade, various dynamic benchmark test functions have been put forward. Generally speaking, though these functions help to improve a lot on the dynamic algorithms design, the fact that the whole landscape might affects the algorithm's performance rather than that of the way it changes is ignored...
A comparative study of θ-PSO and its improved model with partial particles randomization strategy on their abilities of tracking extrema in dynamic environments was carried out in our earlier work. And the results shown that θ-PSO has better performance in dynamic optimization than standard PSO. In this paper, an improved θ-PSO with memory recall and varying scale randomization strategy (θ-PSO-MR)...
Inertia weight can significantly impact the effectiveness of the Particle Swarm Optimization (PSO) algorithm. This paper presents a method to improve performance of PSO optimization algorithm by changing the inertia weight dynamically and adaptively. According to the optimization requirement of PSO algorithm in different situations, the inertia weight in the proposed method can be adjusted automatically...
By providing a detailed analysis of the particle swarm optimization (PSO) principle and job-shop scheduling problems, this paper presents a new hybrid discrete GAPSO combining the genetic strategy. Adjusting factors are introduced to regulate the generation of convergence; the proposed algorithm is tested by a set of benchmark problems. The results obtained show good convergence of the algorithm....
Considering the active power loss and voltage deviation, a Multi-objective Particle Swarm Optimization Algorithm (MOPSO) is presented for power system reactive power dispatch. MOPSO incorporates non-dominated sorting, crowding distance and a special mutation operation into particle swarm optimization to enhance the exploratory capability of the algorithm and improve the diversity of the Pareto solutions...
The application of LQR controller in inverted pendulum system is common, but the choice of weight matrix Q and R of LQR controller has always been a difficult. This paper presents a new method to achieve the double inverted pendulum system LQR controller parameters optimization through the particle swarm optimization algorithm. The simulation result shows that the method used in the choice of matrix...
Acoustic source localization is a very important Wireless Sensor Network (WSN) surveillance task. In real-world implementations, the localization algorithm, which is essentially an optimization of a certain cost function involving all received sensor observations, must be feasible under stringent communication, computation and energy constrains. In this paper, we propose a novel light-weight dynamic...
In this paper, a novel particle swarm optimization (PSO) with dynamic neighborhood topology is considered for large scale optimization. Because the large scale computation problem exists commonly in industry, and is different from the canonical optimization process, solving this problem is imperative. The dynamic neighborhood topology could assist the PSO algorithm cooperate with neighbor particles...
A novel particle swarm optimization (NPSO) algorithm with dynamically changing inertia weight based on fltness and iterations was presented for improving the performance of the Particle Swarm Optimization algorithm. The new algorithm was tested with three benchmark functions. The experimental results show that the swarm can escape from local optimum, and it also can speed up the convergence of particles...
This paper presents a new mathematical formulation for determining optimal Production-inventory policies in the case of dealing with multiple deteriorating products and dynamic demands. The deterioration of each product type occurs at a randomly distributed rate. Production rates for each product type are decision variables, such that process compressibility is assumed to be existed. In other words,...
This paper proposes a new application of a dynamic particle swarm optimization (PSO) algorithm for parameter estimation of an induction machine. The dynamic PSO is one of the PSO variants, which modifies the acceleration coefficients of the cognitive and social components in the velocity update equation of the PSO as linear time-varying parameters. The acceleration coefficients are varied during the...
In this paper, we present ACPSO a new dynamic image clustering algorithm based on particle swarm optimization. ACPSO can partition image into compact and well separated clusters without any knowledge on the real number of clusters. It uses a swarm of particles with variable number of length, which evolve dynamically using mutation operators. Experimental results on real images demonstrate that the...
To control particles flying inside the limited search space and deal with the problem of slow search speed and premature convergence, this paper applies the theory of topology, and proposes a novel quotient space-based boundary condition by using the properties of quotient space and homeomorphism. In this new method, named QsaBC, Search space-zoomed factor and Attractor are introduced to enhance the...
This paper presents a study of the properties of optimization algorithms for use in cognitive machines through five key measures: (i) speed of convergence, (ii) degree of exploration of the parameter space, (iii) storage and system size, (iv) adaptability, and (v) multi-scale capabilities. Based on these factors, a novel study of the trajectories of a particle in the particle swarm optimization algorithm...
Aiming at solving the problems of unsatisfactory routes planning that are suboptimal to optimal routes planning and dissatisfactory real time routes planning and routes planning of multiple unmanned aerial vehicles (UAVs) cooperation, a cost function of multiple UAVs routes planning is presented after modeling the primary factors influencing on multiple UAVs route planning. Then, Based on particle...
Hydropower station optimal operation is a complex nonlinear combinatorial optimization problem. A novel culture particle swarm optimization algorithm for optimal operation problem in hydropower station is suggested. A local random search operator to achieve knowledge structure in belief space and enhance the population diversity and increase the capacity of global search with the introduction of culture...
The radial basis function (RBF), which is well known dynamic neural network, has been improved to easily apply in dynamic systems identification. However, the RBF weights and thresholds, which are trained by the gradient descent method, will be fixed after the training completing. The adaptive ability is bad. To improve RBF performance of dynamic identification, a self-adaptive particle swarm optimization...
This study considers a functional link neural network (FLNN) structure for identifying nonlinear dynamic systems. We tackle the problem of system identification in noisy environments by introducing an adaptive tuning structure based on individual particle optimization (IPO) for the nonlinear systems identification via functional link neural network. The IPO algorithm is applied in order to train the...
Multi-objective particle swarm optimization is the research hot spot of the intelligent computing field at present, which has a more efficient information sharing mechanism than the genetic algorithm (GA) and artificial immune algorithm (AIA). In allusion to a certain kind of new freight car, the multi-objective optimization design of the transmission is carried out by using multi-objective particle...
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