Accelerated Particle Swarm for Optimum Design of Frame Structures

Talatahari, Siamak; Khalili, E.; Alavizadeh, S. M.

doi:10.1155/2013/649857

Cited by 19 publications

(10 citation statements)

References 29 publications

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“…[18] at Cambridge University in 2007 in order to accelerate the convergence of the algorithm is to use the global best only. PSO and APSO-based optimizations have already been studied by the researchers for optimal design of substation grounding grid [34], performance analysis of MIMO radar waveform [35], design of frame structures [36], dual channel speech enhancement [37] and a faster path planner [38] etc.…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…Starting with a randomly initialized population and moving in randomly chosen directions, each particle moves through the searching A simplified version that could accelerate the convergence of the algorithm is to use the global best only. Thus, in the APSO [36], the velocity vector is generated by a simpler formula as where randn is drawn from (0, 1) to replace the second term. The update of the position is simply like-…”

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

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Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

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Green technologies gain popularity to reduce the pollution and give higher penetration of renewable energy source in the transportation. This research induce that the extensive involvement of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. It is also noticed that daytime charging station are necessary for daily usage of PHEVs due to the limited all-electric-range. Most of the research in the past have been stated that only proper charging control and infrastructure management can assure the larger participation of PHEVs.Therefore, researchers are trying to develop efficient control mechanism for charging infrastructure in order to facilitate upcoming PHEVs penetration in highway. Nevertheless, most of the past researcher already aware with the issue related to intelligent energy management. Yet, these studies could not fill the gap of the problem associated with intelligent energy management and require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. The outcome of this research study provides four optimization techniques that includes Hybrid method within swarm intelligence group for the State-of-Charge (SoC) optimization of PHEVs. The finding of this research simulation results obtained for maximizing the highly nonlinear objective function evaluate the comparative performance of all four techniques in terms of best fitness, convergence speed and computation time. Finally, the hybridization method (PSOGSA) presented in this dissertation uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. This study evaluates the performance of standard PSO, then Accelerated version of PSO (APSO), GSA algorithm and then Hybrid of PSO and GSA. The hybridization method (PSOGSA) uses the advantages of both PSO and GSA optimization and thus produce higher best fitness values. However, PSOGSA method takes much longer computational time than single methods because of incorporating two single methods in one algorithm. This research study suggests that PSOGSA method is a great promise for SoC optimization but it takes much longer computational time.

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Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Section: A Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

et al. 2016

View full text Add to dashboard Cite

show abstract

“…A simplified version, which could accelerate the convergence of the algorithm is to use the global best only. Thus, in the APSO [23], the velocity vector is generated by a simpler formula as where randn is drawn from (0, 1) to replace the second term. The update of the position is simply like (12).…”

Section: Accelerated Particle Swarm Optimizationmentioning

confidence: 99%

“…PSO-and APSO-based optimizations have already been studied by researchers for the optimal design of a substation grounding grid [19], non-convex optimization [20,21], performance analysis of MIMO radar waveform [22], design of frame structures [23], dual channel speech enhancement [24], synthesis gas production [25] and a faster path planner [26]. Specifically, we are investigating the use of the accelerated particle swarm optimization (APSO) method for developing real-time and large-scale optimizations for allocating power.…”

Section: Introductionmentioning

confidence: 99%

Swarm intelligence based State-of-Charge optimization for charging Plug-in Hybrid Electric Vehicles

Rahman

Vasant

Singh

et al. 2015

WIT Transactions on Ecology and the Environment

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Transportation electrification has undergone major changes since the last decade. Success of the smart grid with renewable energy integration solely depends upon the large-scale penetration of Plug-in Hybrid Electric Vehicles (PHEVs) for a sustainable and carbon-free transportation sector. One of the key performance indicators in the hybrid electric vehicle is the State-of-Charge (SoC), which needs to be optimized for the betterment of charging infrastructure using stochastic computational methods. In this paper, a newly emerged accelerated particle swarm optimization (APSO) technique was applied and compared with standard Particle swarm optimization (PSO), considering charging time and battery capacity. Simulation results obtained for maximizing the highly non-linear objective function indicate that APSO achieves some improvement in terms of best fitness and computation time.

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“…The algorithms are attractive and popular as they can responsd to unavoidable disadvantages of classical mathematical programming. The evolutionary algorithms (EAs) can deal with almost all kinds of design problem as their search mechanisms, to some extent, rely on randomisation and need no function derivatives, for example, [1][2][3][4]. Recently, single-objective evolutionary methods that have outstanding performance in several applications are realcode ant colony optimisation (ACOR) [5], covariance matrix adaptation evolution strategy (CMA-ES) [6], and differential evolution (DE) [7].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation

Kunakote

Bureerat

2013

Mathematical Problems in Engineering

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The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous shape and sizing design problems of structures taking into account of structural weight, lateral bucking, natural frequency, and stress. Structural analysis is carried out by using a finite element procedure. The optimum results obtained are compared and discussed. The performance assessment is based on the hypervolume indicator. The performance of the surrogate models for estimating design constraints is investigated. It has been found that, by using a quadratic function surrogate model, the optimiser searching performance is greatly improved.

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Accelerated Particle Swarm for Optimum Design of Frame Structures

Cited by 19 publications

References 29 publications

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Optimal power allocation scheme for plug-in hybrid electric vehicles using swarm intelligence techniques

Swarm intelligence based State-of-Charge optimization for charging Plug-in Hybrid Electric Vehicles

Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation

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