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A novel optimised 4 × 4 microstrip patch antenna array operating in the 28−38 GHz frequency range for fifth generation mobile networks is presented. The proposed structure is fed by the corporate feed network printed on the same side of the substrate. To improve the array radiation characteristics, a defected ground plane structure (DGS), which acts as coupled c-shaped in the ground plane, is used...
An improved MOPSO algorithm based on Cloud Membership is designed to cope with the problem of quantitative ParetoSort,as well as convergence rate and variety in solution distribution. In this paper, logistic mapping is adopted to optimize the initial population. In addition, PSO shares global best solution pool with Cuckoo Search, which enhances the ability of global optimization and cooperation among...
This paper aimed at exploring the performance of Particle Swarm Optimisation with Exponentially Varying Inertia Weight Factor (PSO-EVIWF) for solving Multi-Area Economic Dispatch (MAED) problem with tie line constraints considering valve-point loading in each area. The effectiveness of the proposed algorithm has been verified on 4 interconnected areas with 16 generators standard test system. The paper...
Particle Swarm Optimisation (PSO) algorithm is known to be better than Genetic Algorithm (GA) as fewer operators are needed in its algorithm. However, it still has some weaknesses such as immature convergence; a condition whereby PSO tends to get trapped in a local optimum. This condition prevents them from being converged towards a better position. Various techniques have been proposed to tackle...
Particle swarm optimization (PSO) algorithms are now being practiced for more than a decade and have been extended to solve various types of optimization problems. However, straightforward application of PSO suffers from premature convergence and lacks of intensification around the local best locations. In this paper, we propose a new particle swarm optimization strategy, namely, particle swarm optimization...
This paper proposes new optimization algorithms for the optimal tuning of PI controllers dedicated to a class of second-order processes with integral component and variable parameters. The sensitivity analysis with respect to the parametric variations of the controlled process leads to the sensitivity models. The augmentation of the output sensitivity functions over the integral of absolute error...
Due to the fast convergence, Particle swarm optimization (PSO) has been advocated to be especially suitable for multiobjective optimization. However, there is no information-sharing of with other particles in the population, except that each particle can access the global best. Thus, the premature convergence and lacks of intensification around the local best locations are inevitable during extending...
Recent research on particle swarm optimization (PSO) emphasizes the need to simply this algorithm. This paper is a short study on finding the minimal number of particles in a simplified PSO. We have taken into consideration a social-only variant, Pedersen's simplified PSO, and tested it with four popular optimization benchmark functions in order to discover which is the minimal number of particles...
Economic Load Dispatch is one of the most important tasks to be performed in the operation and planning of a power system that decides the generation schedule of generating units with an objective of minimizing the total fuel cost. Normally, the fuel cost of generators can be treated as a quadratic function of real power generation. In fact, valve point loading effect in thermal power plants introduces...
In this paper, we present a new particle swarm optimization. The original PSO has a weight term which is decreasing, increasing, or constant during iterations. In this paper, inertia terms are a vector instead of a scalar. Comparing a velocity and an updating term, the weight can be increased or decreased. That is, if the absolute value of velocity is larger or lesss than that of the update term,...
In this paper, we present PID controller design methods for automatic voltage regulators. We use three improved particle swarm optimization for PID controllers with which the step response is optimally regulated for automatic voltage controllers. We compare three different versions of particle swarm optimizations, i.e., the modified original PSO, the crazy PSO and the chaotic PSO. Among three PSOs,...
In this paper, considering both of access efficiency, order-picking and storage-space cost, a Evolutionary Algorithm Inspired Particle Swarm Optimization (EA-PSO) Algorithm is developed to solve the Warehouse Allocation Problem. Based on the Warehouse Storage Policy, a Class-based storage method is proposed. Moreover, during the procedures goods are ordered by their cube per order index (COI), and...
Economic Load Dispatch is one of the major functions of modern Energy Management System (EMS), which determines the optimal real power settings of generating units with an objective of minimizing the total fuel cost. All industrial practice, the fuel cost of generators can be treated as a quadratic function of real power generation. In fact, valve point loading effect in thermal power plants calls...
This paper describes the evaluation of the spreading factor inertia weight Particle Swarm Optimization (PSO) for the fuzzy logic control (FLC) of FES-assisted paraplegic indoor rowing exercise (FES-rowing). The FES-rowing is introduced as a total body exercise for rehabilitation of lower extremities through the application of functional electrical stimulation (FES). FLC is used to control the knee...
In this paper a hybrid model which is a combination of Memetic algorithm, cellular learning automata (CLA) and PSO is proposed. The proposed algorithm in addition to maintaining diversity; largely reduces the probability of getting trap in local optima. Experimental results on eight optimization problems show the superiority of the proposed algorithm.
This paper presents an improved particle swarm optimization algorithm (IPSO) to optimal reactive power dispatch and voltage control of power system. The improved particle swarm optimization algorithm introduces the adaptive inertia factor into the classical swarm algorithm to improve the global search capability and accelerate the searching speed. Study the DPSO algorithm to construct the collection...
State space pruning is a methodology that has been successfully applied to improve the computational efficiency and convergence of Monte Carlo Simulation (MCS) when computing the reliability indices of composite power systems. This methodology increases performance of MCS by pruning state spaces in such a manner that a new state space with a higher density of failure states than the original state...
Normalized radial basis function (NRBF) neural network is presented to directly approach the Q-value function and generalize the information learnt by learning agent in continuous space. The action which impacts on environment is the one with maximum output of NRBF in the current state, and generated through Quantum-Behaved Particle Swarm Optimizer based on the current state. The effectiveness of...
In many applications it is desirable to have the maximum radiation of an array directed normal to the axis of the array. In this paper, the broadside radiation patterns of three-ring Concentric Circular Antenna Arrays (CCAA) with central element feeding are reported. For each optimal synthesis, optimal current excitation weights and optimal radii of the rings are determined having the objective of...
Like many wireless systems, Orthogonal Frequency Division Multiplexing (OFDM) needs proper allocation of limited resources such as total transmit power and available frequency bandwidth among the users to meet their service requirements. In this paper, different versions of two evolutionary approaches, Differential Evolution (DE) and Particle Swarm Optimization (PSO) have been applied for adaptive...
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