Search Results - (( programming using meta algorithm ) OR ( java application stemming algorithm ))

A new meta heuristic evolutionary programming (NMEP) in optimizing economic energy dispatch by Mohamad Ridzuan, Mohamad Radzi, Hassan, Elia Erwani, Abdullah, Abdul Rahim, Bahaman, Nazrulazhar, Abdul Kadir, Aida Fazliana

“…The proposed optimization algorithm, namely New Meta-Heuristic Evolutionary Programming (NMEP) algorithm is followed to Meta-Heuristic Evolutionary Programming (Meta-EP) approach with some modification where the cloning process embedded as a significant progress during the implementation. …”
Get full text
Get full text
Get full text

An Environmentally Energy Dispatch Using New Meta Heuristic Evolutionary Programming by Mohamad Ridzuan, Mohamad Radzi

“…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.…”
Get full text
Get full text
Get full text
Get full text

A hybrid intelligent algorithm for solving the bilevel programming models by Shamsul Bahar, Yaakob, Prof. Madya, Watada, Junzo

Get full text

Performance evaluation of task scheduling using hybrid meta-heuristic in the heterogeneous cloud environment by Mohammed, Faten Ameen Saif

Get full text
Get full text

Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach by Lai, V., Ahmed, Ali Najah, Malek, Marlinda Abdul, El-Shafie, Ahmed, El-Shafie, Amr

“…Genetic Programming (GP) algorithm is an example of an evolutionary algorithm (EA) in the field of evolutionally computation (EC) and, more broadly, in Artificial Intelligence. …”
Get full text
Get full text

Elitist hybrid migrating birds optimization and genetic algorithm based strategy for T-way test suite generation by Hasneeza Liza, Zakaria

“…Although useful, much existing meta-heuristic based strategies have not sufficiently explored the adoption of more than one meta-heuristic to perform the search (termed hybridization). …”
Get full text
Get full text

Review on Dam and Reservoir Optimal Operation for Irrigation and Hydropower Energy Generation Utilizing Meta-Heuristic Algorithms by Chong K.L., Lai S.H., Ahmed A.N., Zaafar W.Z.W., Rao R.V., Sherif M., Sefelnasr A., El-Shafie A.

Review on Dam and Reservoir Optimal Operation for Irrigation and Hydropower Energy Generation Utilizing Meta-Heuristic Algorithms by Chong K.L., Lai S.H., Ahmed A.N., Jaafar W.Z.W., Rao R.V., Sherif M., Sefelnasr A., El-Shafie A.

An analysis on the applicability of meta-heuristic searching techniques for automated test data generation in automatic programming assessment by Musa, Ja’afar, Romli, Rohaida, Yusoff, Nooraini

“…Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. …”
Get full text
Get full text
Get full text

Optimal operation of stand-alone microgrid considering emission issues and demand response program using whale optimization Algorithm by Tahmasebi M., Pasupuleti J., Mohamadian F., Shakeri M., Guerrero J.M., Basir Khan M.R., Nazir M.S., Safari A., Bazmohammadi N.

Cyclic Path Planning Of Hyper-Redundant Manipulator Using Whale Optimization Algorithm by Machmudah A., Parman S., Abbasi A., Solihin M.I., Manan T.S.A., Beddu S., Ahmad A., Rasdi N.W.

Formulating and solving stochastic truck and trailer routing problems using meta-heuristic algorithms / Seyedmehdi Mirmohammadsadeghi by Seyedmehdi, Mirmohammadsadeghi

Get full text
Get full text
Get full text

Solving the integrated inventory supply chain problems using meta-heuristic methods / Seyed Mohsen Mousavi by Seyed Mohsen , Mousavi

“…As the mixed integer nonlinear model of the problem was complicated to solve using exact methods, several meta-heuristic algorithms were employed in to optimize the models under investigation. …”
Get full text
Get full text
Get full text

Evolutionary algorithm for forecastng mean sea level based on meta-heuristic approach by Lai V., Ahmed A.N., Malek M.A., El-Shafie A., El-Shafie A.

“…Genetic Programming (GP) algorithm is an example of an evolutionary algorithm (EA) in the field of evolutionally computation (EC) and, more broadly, in Artificial Intelligence. …”

Multi-Objective Network Reconfiguration with Optimal DG Output Using Meta-Heuristic Search Algorithms by Badran, Ola, Mokhlis, Hazlie, Mekhilef, Saad, Dahalan, Wardiah Mohd

Get full text
Get full text

Grey Wolf Optimizer Based Battery Energy Storage System Sizing for Economic Operation of Microgrid by Sukumar S., Marsadek M., Ramasamy A., Mokhlis H.

Optimal coordination of overcurrent relay protection using evolutionary programming / Noor Shah Rizal Abdul Manap by Noor Shah Rizal, Abdul Manap

“…Due to the difficulty of existing techniques employing different types of algorithm, the usual optimal coordination of overcurrent relays is generally carried out by heuristic, meta-heuristic optimization methods. …”
Get full text
Get full text
Get full text

Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation by Dreidy, M., Mokhlis, Hazlie, Mekhilef, Saad

“…Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS) scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP), Binary genetic algorithm (BGA), and Binary particle swarm optimization (BPSO), to determine the optimal combination of loads that needs to be shed from the islanded distribution network. …”
Get full text
Get full text

An alternative method to solve combined economic emission dispatch problems using flower pollination algorithm by Hong, Mee Song

“…Flower Pollination Algorithm (FPA) is a new biologically inspired meta-heuristic optimization technique based the pollination process of flowers. …”
Get full text
Get full text

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications. by S. Ahmed, Bestoun, Enoiu, Eduard, Afzal, Wasif, Kamal Z., Zamli

“…Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. …”
Get full text
Get full text
Get full text