Multi-objective feasibility enhanced particle swarm optimization

Hasanoglu, Mehmet Sinan; Dölen, Melik

doi:10.1080/0305215x.2018.1431232

Cited by 41 publications

(22 citation statements)

References 36 publications

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“…Çok amaçlı ve tek amaçlı yaklaşımların kıyaslanması için SRN [23], RC-MOG (İng. reduced coonstraints multiobjective gear train) [5], ve MV-MOG (İng. mixed valued multi-objective gear train) [5] problemleri kullanılmıştır.…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

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Olurluğu arttırılmış parçacık sürü optimizasyonunda çok amaçlı ve tek amaçlı yaklaşımların kıyaslanması

Hasanoglu

Dölen²

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Highlights:Graphical/Tabular Abstract  Multi-objective and singleobjective feasibility enhanced particle swarm optimization  Pareto optimality based multi-objective optimization approach  Fixed weight linearly aggregated objective functions Figure A. Solutions obtained by the multi-objective approach provides design alternatives in the solution domain whereas the single objective approach gives a single solution for the given criteriaPurpose: Comparison of a Pareto based multi-objective feasibility enhanced particle swarm optimization approach with fixed weight linearly aggregated single-objective optimization using a variant of the same algorithm. Evaluation of the advantages presented by the multi-objective approach. Theory and Methods:Solutions for three separate multi-objective constrained problems were obtained using two different approaches: Pareto based multi-objective feasibility enhanced particle swarm optimization Singleobjective feasibility enhanced particle swarm optimization using fixed weight linearly aggregated singleobjective variants of the problems. Results:Comparisons involving three problems (two of which were highly constrained) revealed that optimizations performed using the multi-objective approach resulted in solutions that were also suitable for all singleobjective criteria. Results obtained by a single multi-objective optimization run were found to be at least as good as results obtained by separate aggregated-objective runs for each aggregated criterion. Conclusion:In Pareto optimality based optimization algorithms a Pareto front consisting of many non-dominated solutions are found in each run instead of a single solution. Therefore: The designer can choose from these non-dominated solutions with a clear trade-off between known values of all objectives. This approach allows performing the trade-off without requiring rerunning the optimization with new objective weights. If efficient Pareto optimality based multi-objective optimization algorithms are available, it is unnecessary to employ single-objective approaches by combining the objectives with presumably biased weights assigned with insufficient information related with the problem.

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Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

“…reduced coonstraints multiobjective gear train) [5], ve MV-MOG (İng. mixed valued multi-objective gear train) [5] problemleri kullanılmıştır. SRN problemi (Eş.…”

Section: Sonuçlar Ve Tartişmalar (Results and Discussion)unclassified

Olurluğu arttırılmış parçacık sürü optimizasyonunda çok amaçlı ve tek amaçlı yaklaşımların kıyaslanması

Hasanoglu

Dölen²

2019

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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“…Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions [16], [17] and [18]. c -Ant Colony Optimization (ACO): the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.…”

Section: B -Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm

Lateef

Burhan

2019

Civ Eng J

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The cost of pile foundations is part of the super structure cost, and it became necessary to reduce this cost by studying the pile types then decision-making in the selection of the optimal pile type in terms of cost and time of production and quality .So The main objective of this study is to solve the time–cost–quality trade-off (TCQT) problem by finding an optimal pile type with the target of "minimizing" cost and time while "maximizing" quality. There are many types In the world of piles but in this paper, the researcher proposed five pile types, one of them is not a traditional, and developed a model for the problem and then employed particle swarm optimization (PSO) algorithm, as one of evolutionary algorithms with the help of (Mat lab software), as a tool for decision making problem about choosing the best alternative of the traded piles, and proposes a multi objective optimization model, which aims to optimize the time, cost and quality of the pile types, and assist in selecting the most appropriate pile types. The researcher selected 10 of senior engineers to conduct interviews with them. And prepared some questions for interviews and open questionnaire. The individuals are selected from private and state sectors each one have 10 years or more experience in pile foundations work. From personal interviews and field survey the research has shown that most of the experts, engineers are not fully aware of new soft wear techniques to helps them in choosing alternatives, despite their belief in the usefulness of using modern technology and software. The Problem is multi objective optimization problem, so after running the PSO algorithm it is usual to have more than one optimal solution, for five proposed pile types, finally the researcher evaluated and discussed the output results and found out that pre-high tension spun (PHC)pile type was the optimal pile type.

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“…Therefore, in this paper, multi-objectives feasibility enhanced particle swarm optimization (MOFEPSO) is used to find the aforementioned three solutions from the new Pareto frontier. MOFEPSO is used in this paper as it gradually increases the entire feasibility of the swarms to obtain superior solutions [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm

et al. 2022

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This paper aims to investigate a hybrid photovoltaic (PV) biogas on-grid energy system in Al-Ghabawi territory, Amman, Jordan. The system is accomplished by assessing the system’s reliability and economic viability. Realistic hourly measurements of solar irradiance, ambient temperature, municipal solid waste, and load demand in 2020 were obtained from Jordanian governmental entities. This helps in investigating the proposed system on a real megawatt-scale retrofitting power system. Three case scenarios were performed: loss of power supply probability (LPSP) with total net present cost (TNPC), LPSP with an annualized cost of the system (ACS), and TNPC with the index of reliability (IR). Pareto frontiers were obtained using multi-objective feasibility enhanced particle swarm optimization (MOFEPSO) algorithm. The system’s decision variables were the number of PV panels (Npv) and the number of biogas plant working hours per day (tbiogas). Moreover, three non-dominant Pareto frontier solutions are discussed, including reliable, affordable, and best solutions obtained by fuzzy logic. Double-diode (DD) solar PV model was implemented to obtain an accurate sizing of the proposed system. For instance, the best solution of the third case is held at TNPC of 64.504 million USD/yr and IR of 96.048%. These findings were revealed at 33,459 panels and 12.498 h/day. Further, system emissions for each scenario have been tested. Finally, decision makers are invited to adopt to the findings and energy management strategy of this paper to find reliable and cost-effective best solutions.

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Multi-objective feasibility enhanced particle swarm optimization

Cited by 41 publications

References 36 publications

Olurluğu arttırılmış parçacık sürü optimizasyonunda çok amaçlı ve tek amaçlı yaklaşımların kıyaslanması

Olurluğu arttırılmış parçacık sürü optimizasyonunda çok amaçlı ve tek amaçlı yaklaşımların kıyaslanması

Time-Cost-Quality Trade-off Model for Optimal Pile Type Selection Using Discrete Particle Swarm Optimization Algorithm

Optimal Allocation of a Hybrid Photovoltaic Biogas Energy System Using Multi-Objective Feasibility Enhanced Particle Swarm Algorithm

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