A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA

Zhang, Qiang; Ogren, Ryan M.; Kong, Song‐Charng

doi:10.1016/j.apenergy.2015.12.044

Cited by 87 publications

(27 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, by using the PSO-generated initial population with sufficient fitness for the GA, the convergence was effectively accelerated, and the search space was expanded [30,31]. The optimized convergence in case studies revealed that because particles easily found the optimal solution during the early stage of evolution, they frequently moved large distances.…”

Section: Particle Swarm Optimization-genetic Algorithmmentioning

confidence: 99%

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Fang

Chung

2017

Energies

View full text Add to dashboard Cite

Abstract:The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. Electric-bus (e-bus) transportation systems help reduce pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO-GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO-GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was shown to be lower than that of the battery-swapping e-bus system.

show abstract

Section: Particle Swarm Optimization-genetic Algorithmmentioning

confidence: 99%

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Fang

Chung

2017

Energies

View full text Add to dashboard Cite

show abstract

“…Zhang et al [61] 2015 PSO-GA Biodiesel Engine Performance Optimization Li et al [62] 2018 PSO-GA Optimization of a heliostat field layout…”

Section: Author/smentioning

confidence: 99%

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Sengupta

Basak

Peters

2018

MAKE

394

184

View full text Add to dashboard Cite

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social cooperation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the very recent state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.

show abstract

“…Using the same idea as Harish Garg (PSO algorithm is used to select better individuals of the initial iteration before performing steps of evolution), Q. Zhang et al [15] provided a simpler algorithm to optimize the parameters of direct-injection diesel engine running with soy biodiesel. The PSO algorithm is done on the n best individuals, thus producing n offspring for use in the next iteration (generation).…”

Section: Selecting the Hybrid Ga-pso Algorithmmentioning

confidence: 99%

“…On the other hand, the precision is affected by many criteria, one of which is the selected method or algorithm. Hybrid methods are described in [14,15]. H. Garg [14] used GA-PSO to solve a nonlinear optimal problem.…”

Section: Introductionmentioning

confidence: 99%

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Pham

Nguyen

et al. 2019

Energies

View full text Add to dashboard Cite

Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

show abstract

A comparative study of biodiesel engine performance optimization using enhanced hybrid PSO–GA and basic GA

Cited by 87 publications

References 40 publications

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Minimization of Construction Costs for an All Battery-Swapping Electric-Bus Transportation System: Comparison with an All Plug-In System

Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Contact Info

Product

Resources

About