Design of Optimal Two-Dimensional FIR Filters with Quadrantally Symmetric Properties Using Vortex Search Algorithm

2019 ◽  
Vol 29 (10) ◽  
pp. 2050155 ◽  
Author(s):  
Suman Yadav ◽  
Richa Yadav ◽  
Ashwni Kumar ◽  
Manjeet Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Finite Impulse Response ◽  
Search Algorithm ◽  
Stop Band ◽  
Particle Swarm ◽  
Pass Band ◽  
Swarm Optimization ◽  
Genetic Search ◽  
Hybrid Particle Swarm Optimization ◽  
Real Coded Genetic Algorithm

This research paper presents a new evolutionary technique named vortex search optimization (VSO) to design digital 2D finite impulse response (FIR) filter for improved performance both in pass-band and stop-band regions. Optimum filter coefficients are calculated by minimizing the deviation of actual frequency response from specified or desired response. Efficiency of the designed filter is measured by several parameters, such as maximum pass-band ripple, maximum stop-band ripple, mean attenuation in stop band and time taken, to execute the code. Analysis of the performance of designed filter is correlated with various different algorithms like real coded genetic algorithm, particle swarm optimization, genetic search algorithm and hybrid particle swarm optimization gravitational algorithm. Comparative study shows significant reduction in pass-band error, stop-band error and execution time.

scholarly journals An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3052 ◽  
Author(s):  
Ali Ahmadian ◽  
Ali Elkamel ◽  
Abdelkader Mazouz
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Combinatorial Problem ◽  
Distribution Networks ◽  
Swarm Optimization ◽  
Expansion Planning ◽  
Hybrid Particle Swarm Optimization ◽  
Electric Distribution

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems

2011 ◽  
Vol 15 (4) ◽  
pp. 473-478
Author(s):  
Shamshul Bahar Yaakob ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Method ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Selection Problems ◽  
Hybrid Particle Swarm Optimization ◽  
Trade Offs

In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposed method is a viable approach to portfolio selection.

scholarly journals Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Shanhe Jiang ◽  
Chaolong Zhang ◽  
Shijun Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Particle Swarm ◽  
Random Coefficients ◽  
Superior Performance ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization ◽  
Gravitational Search

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

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