Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization

Dash, Judhisthir; Dam, Bivas; Swain, Ramakrushna

doi:10.1016/j.aeue.2019.153019

Cited by 24 publications

(12 citation statements)

References 35 publications

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“…Too et al (2019) invented BPSODE where the strength of binary PSO (BPSO) and binary DE (BDE) are inherited and computed in sequence. Dash et al (2020) proposed HDEPSO in which three DE operations (mutation, modified crossover and selection) are fused with the best particles of PSO for enhancing global searching ability. Parouha and Verma (2021) proposed innovative hybrid algorithm of DE and PSO for bound-unconstrained optimization and ELD problem with or without valve point effects.…”

Section: Masmentioning

confidence: 99%

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

Verma

Parouha

2022

Soft Comput

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As environmental concerns have grown, the combined economic emission dispatch (CEED) problem has gotten a lot of attention. Both the cost of fuel and the emission pollution caused by it must be kept to a minimum. As a result, this paper presents an innovative hybrid approach (ihPSODE) for solving CEED problems. This hybrid technique incorporated novel differential evolution (nDE) and particle swarm optimization (nPSO). Where nDE introduces a new mutation approach and crossover rate (to prevent premature convergence) as well as nPSO introduces a new acceleration coefficient, inertia weight and position improve equation (to alleviate the stagnation). So as to balance among local and global search ability, after ihPSODE population evaluation, the best half individuals are determined and the rest individuals are discarded. Then, nPSO is used in the current population (to sustain exploration and exploitation) and nDE is employed in the nPSO generated population (to improve convergence accuracy). The competence of the proposed algorithms (ihPSODE, nPSO and nDE) are inspected on 23 unconstrained benchmark function and then solved 3 test system (3-, 6-and 40-unit) of economic load dispatch (ELD) and 3 test system (3-, 10-and 40-unit) of CEED problem. The experiments have denoted that the proposed algorithms show competitive results and significant performances.

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Section: Masmentioning

confidence: 99%

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

Verma

Parouha

2022

Soft Comput

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“…52,53 Because of different optimization algorithms have different search behaviors and advantages. Therefore, in order to enhance performance of DE and PSO, lots of its hybrid algorithms are presented in the literature as follows: DEPSO, 54 DE-PSO, 55 evolving cognitive and social experience in PSO through DE, 56 HPSDE, 57 DEPSO, 58 HPSO-DE, 59 DEPSO, 60 DPD, 61 HNTVPSO-RBSADE, 62 MBDE, 63 DE-PSO-DE, 64 DE-PSO, 65 DEMPSO, 66 SAPSO-mSADE, 67 BPSODE, 68 HDEPSO, 69 and improved QPSO. 70 Nevertheless, to overcome their individual shortcomings, either the solution leads to a premature convergence or stacks at some local optima, hybrid techniques are now more favored over their individual effort.…”

Section: Related Workmentioning

confidence: 99%

An advanced hybrid algorithm for nonlinear function optimization with real world applications

Verma

Parouha

2021

Concurrency and Computation

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This article presents an advanced hybrid algorithm (haDEPSO) for nonlinear constrained function optimization problem. It consists of the advised advanced differential evolution, that is, aDE (wherein a novel mutation, crossover, and selection strategy is introduced, to avoid premature convergence) and particle swarm optimization, namely, aPSO (in which novel gradually varying parameters is utilized, to escape stagnation).The proposed haDEPSO achieve better results as aDE and aPSO provides diverse convergence characteristic to the solution space. Moreover, in haDEPSO distinct population is merged with other in a predefined way, to balance between exploration and exploitation. Efficiency of the proposed hybrid haDEPSO along with its integrating component aDE and aPSO are inspected on IEEE CEC'2006 constrained benchmark functions and IEEE CEC'2011 real world problems. According to the comparison results, the proposed methods achieved better results than traditional DE and PSO with their hybrids as well as over many state-of-the-art algorithms.

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“…It has been highlighted by [5,20] that the MOEA/D does not produce good quality and a well-distributed optimization in problems with complicated Pareto optimal front shapes. Such problems are problems with long tail or sharp peak [5,20], which qualifies the complexity of real-world problems, like the Sharp Edge FIR Filters Design problem [21], Mitigation of Power Oscillations problem [22], and a Long Tail problem in Recommender System [23]. Several researchers proposed improvements on the MOEA/D to overcome the issues mentioned above.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

Saveca

Sun

Wang

2022

International Transactions on Electrical Energy Systems

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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has attracted a lot of attention since it can handle multiobjective problems (MOP) with a complicated Pareto front. The procedure involves decomposing a MOP into single subproblems, which are eventually optimized simultaneously based on the MOP neighborhood information. However, the MOEA/D strategy tends to produce a distributed optimization that is not of good quality in some problems with complex Pareto optimal front, such as problems with a long tail and sharp peak, common in real-world situations. This paper proposes an improved MOEA/D to enhance the distributed optimization quality and minimize its complexity while accelerating the optimization to get a better solution. The improved method is achieved by incorporating a Hybrid Differential Evolution/Particle Swarm Optimization algorithm and a hybrid operator based on nondominated sorting and crowding distance algorithm. This incorporation takes place in the mutation generator and initial population part of the original MOEA/D algorithm. Simulations and comparisons are carried out based on some MOP benchmark functions to verify the proposed method’s performance. The experimental results show that the proposed method achieves better performance compared to other algorithms. Furthermore, the proposed method is also applied to optimize the multiobjective wave energy converter model to maximize power per year and minimize cost per unit power.

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Design and implementation of sharp edge FIR filters using hybrid differential evolution particle swarm optimization

Cited by 24 publications

References 35 publications

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

An innovative hybrid algorithm for solving combined economic and emission dispatch problems

An advanced hybrid algorithm for nonlinear function optimization with real world applications

A Hybrid Multiobjective Optimization Based on Nondominated Sorting and Crowding Distance, with Applications to Wave Energy Converters

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