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Multiobjective optimization using weighted sum Artificial Bee Colony algorithm for Load Frequency Control
Published 2014“…This paper presents the implementation of multiobjective based optimization of Artificial Bee Colony (ABC) algorithm for Load Frequency Control (LFC) on a two area interconnected reheat thermal power system. …”
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Network reconfiguration and control for loss reduction using genetic algorithm
Published 2010“…The proposed solution to this problem is based on a general combinatorial optimization algorithm known as Genetic Algorithm, and the load flow equations in distribution network. …”
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…The proposed algorithm is simulated for simultaneous OPF-based conflicting objectives, respectively. …”
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An Optimal Load Shedding Methodology for Radial Power Distribution Systems to Improve Static Voltage Stability Margin using Gravity Search
Published 2014“…In this paper, an effective method is presented for estimating the optimal amount of load to be shed in a distribution system based on the gravitational search algorithm (GSA). …”
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Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network
Published 2022“…Finally, an AHP was integrated with FA to form Firefly Analytical Hierarchy Algorithm (FAHA) to automatically calculate the weight of each objective function based on the load flow outputs followed by the optimisation process. …”
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Optimal economic load dispatch using multiobjective cuckoo search algorithm
Published 2023“…In this paper, Multiobjective Cuckoo Search Algorithm (MOCSA) is developed to solve Economic Load Dispatch (ELD) problem. …”
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Review on Dam and Reservoir Optimal Operation for Irrigation and Hydropower Energy Generation Utilizing Meta-Heuristic Algorithms
Published 2023“…Biomimetics; Computational efficiency; Dams; Electric load dispatching; Heuristic algorithms; Heuristic methods; Hydroelectric power; Hydroelectric power plants; Irrigation; Mathematical programming; Scheduling; Structural design; Water distribution systems; Water resources; Water supply; Application problems; Hydro-power generation; Meta heuristic algorithm; Non-differentiability; Problem formulation; Reservoir optimal operation; Reservoir optimizations; Scientific discipline; Reservoirs (water)…”
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Review on Dam and Reservoir Optimal Operation for Irrigation and Hydropower Energy Generation Utilizing Meta-Heuristic Algorithms
Published 2023“…Biomimetics; Computational efficiency; Dams; Electric load dispatching; Heuristic algorithms; Heuristic methods; Hydroelectric power; Hydroelectric power plants; Irrigation; Mathematical programming; Scheduling; Structural design; Water distribution systems; Water resources; Water supply; Application problems; Hydro-power generation; Meta heuristic algorithm; Non-differentiability; Problem formulation; Reservoir optimal operation; Reservoir optimizations; Scientific discipline; Reservoirs (water)…”
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Performance Improvement of Multiobjective Optimal Power Flow-Based Renewable Energy Sources Using Intelligent Algorithm
Published 2022“…Producing energy from a variety of sources in a power system requires an optimal schedule to operate the power grids economically and efficiently. …”
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Controller placement problem in the optimization of 5G based SDN and NFV architecture
Published 2021“…If the control plane fails to improve system resilience quality, A reliable RAMCP is formulated as an optimal solution for fault tolerance. …”
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Loss reduction in distribution networks using new network reconfiguration algorithm
Published 1996“…The solution algorithm of the proposed method has been tested, with very promising results, on a 69-bus radial distribution system. …”
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Optimization of operating cost and energy consumption in a smart grid
Published 2024“…The non-dominated sorting genetic algorithm II (NSGA-II) is employed to address optimization challenges. …”
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Load dispatch optimization of open cycle industrial gas turbine plant incorporating operational, maintenance and environmental parameters
Published 2006“…The objective of this work is to develop a multi-objective optimization model and optimization algorithm for load dispatching optimization of open cycle gas turbine plant that not only consider operational parameters, but also incorporates maintenance and environmental parameters. …”
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Design of single- and multi-loop self-adaptive PID controller using heuristic based recurrent neural network for ALFC of hybrid power system
Published 2022“…Then, these dynamics are solved using hybrid particle swarm optimization-gravitational search algorithm (PSO-GSA) to obtain the initial solution. …”
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An Environmentally Energy Dispatch Using New Meta Heuristic Evolutionary Programming
Published 2018“…Basically,one important issue in the power system network is to provide the optimal Economic Load Dispatch (ELD) solution in order to guarantee the sustainable consumer load demand.However,today ELD solution is essential to include together with the environmental aspect and known as Environmental Economic Load Dispatch (EELD).For that reason, many researchers continue in the development of new simulation tool specifically to overcome the EELD problems.Therefore,this study prepared an improved hybrid metaheuristic technique named as New Meta Heuristic Evolutionary Programming (NMEP) to provide the best possible solution in solving the identified single objective and multi objective functions for EELD solution.This new technique a merging cloning strategy that involved in an Artificial Immune System (AIS) algorithm into algorithm of Meta Heuristic Evolutionary Programming (Meta-EP).The development of NMEP technique is to minimize total cost,reduce the total emission during generator operation through the common formula in EELD and lowest total system loss.Besides that,all mentioned objective functions were also optimized together simultaneously that formulated using the weighted sum method before had been executed on the multi objective NMEP or called MONMEP.Both individual and multi objective NMEP techniques performance were verified among other two common heuristic methods known as AIS and Meta-EP techniques.In addition,the best possible solution defined using the aggregate function method.Through this method,the selection of the best MOEELD solution became effortless as compared with MO individually that required compare two or more objective function in one time manually.Among those three optimization techniques the lowest total aggregate values mostly resulted via the NMEP technique.Based upon that,the proposed technique is proving as the outstanding method compared with Meta-EP and AIS techniques in solving the EELD problem for both standard IEEE 26 bus and 57 bus systems.…”
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Loss reduction in distribution networks by network reconfiguration: a two stage solution approach
Published 2004“…The solution algorithm for loss minimization has been developed based on a two-stage solution methodology. …”
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Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model
Published 2015“…For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique. …”
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Capacity Planning For Mixed-Load Tester Under Demand And Testing Time Uncertainty
Published 2018“…Currently,the company’s issue is low tester utilization of about 71%,well below the target of 96%.The objective of this research is to improve tester utilization while achieving the production target under uncertain demand and testing time and also to determine the break-even point on the testers required.A novel approach of integrating a mathematical model,robust optimization model,genetic algorithm,simulation model and cost–volume –profit analysis was developed.Firstly,a mathematical model of mixed-load tester was formulated.Next,a set of discrete scenarios was proposed to address uncertain demand and testing time.A robust optimization and genetic algorithm model was developed to optimize the number of testers under the described uncertainties.Next,these scenarios were simulated using the Pro Model simulation software to validate the proposed models and to evaluate throughput and tester utilization.Finally,the cost–volume–profit analysis was performed for scenarios that require additional testers at various levels of uncertainties.The results showed that the proposed solution improved tester utilization by 25% compared to the current system.This research has contribution by developing novel hybrid methodology and able to provide useful insights to assist company’s managers to plan and allocate resources according to variations in customers’ demands and testing time.…”
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