Search Results - (( evolution optimisation based algorithm ) OR ( problem implementation case algorithm ))

Optimal placement, sizing and power factor of distributed generation: A comprehensive study spanning from the planning stage to the operation stage by Huy P.D., Ramachandaramurthy V.K., Yong J.Y., Tan K.M., Ekanayake J.B.

Performance evaluation and benchmarking of an extended computational model of ant colony system for DNA sequence design by Zuwairie, Ibrahim, Mohd Falfazli, Mat Jusof, Mohd Zaidi, Mohd Tumari

“…Ant colony system (ACS) algorithm is one of the biologically inspired algorithms that have been introduced to effectively solve a variety of combinatorial optimisation problems. …”
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A competitive co-evolutionary approach for the nurse scheduling problem by Mohamad Nazri, Maizatul Farhana, Mohd Yusoh, Zeratul Izzah, Basiron, Halizah, Daud, Azlina

“…The competitive approach further exhibits smoother convergence behaviour across generations, indicating stronger optimisation dynamics and improved robustness. These findings demonstrate that competitive co-evolution provides an effective and practical alternative to static fitness-based evolutionary methods for nurse scheduling, with broader applicability to healthcare scheduling and constraint optimisation problems.…”
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An experimental study of neighbourhood based metaheuristic algorithms for test case generation satisfying the modified condition / decision coverage criterion by Haque, Ariful

“…We have chosen four neighborhood based algorithms which are commonly used in optimization problems and divided them in newly implemented and re-implemented category. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

“…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom by Masrom, Suraya

“…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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Stock market turning points rule-based prediction / Lersak Photong … [et al.] by Photong, Lersak, Sukprasert, Anupong, Boonlua, Sutana, Ampant, Pravi

“…Finally, rule-based optimisation techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE) and Grey Wolf Optimizer (GWO) were used to minimise the amount of time employed in the stock market turning points prediction. …”
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Fast Finite Difference Time Domain Algorithms for Solving Antenna Application Problem by Hasan, Mohammad Khatim

“…This thesis describes the implementations of new parallel and sequential algorithms for electromagnetic wave propagation from a monopole antenna. …”
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Implementation of case-based reasoning in help desk system by Tee, Shih Min

“…Technicians apply CBR technique to solve a given problem by retrieving the precedent similar cases that are stored into the case library. …”
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Low-level hybridization scripting language with dynamic parameterization in PSO-GA / Suraya Masrom by Masrom, Suraya

“…However, in many cases, implementing the suitable hybrid algorithms for a given optimization problem is a considerably difficult, which in most cases, is time consuming. …”
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A trade-off criterion for bi-objective problem in solving hybrid flow shop scheduling with energy efficient (EE-HFS) using multi-objective dragonfly algorithm (MODA) by Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

“…This paper presents an optimization scheme for a case study on minimizing makespan and energy consumption in a hybrid flow shop scheduling problem. …”
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Quantum Processing Framework And Hybrid Algorithms For Routing Problems by Soltan Aghaei, Mohammad Reza

“…The designed hybrid architecture and framework were tested using the above quantum algorithms and the existing quantum algorithms. Implementing and simulating results are then represented based on the probabilistic hybrid algorithm in the case of linear array, binary heap and quantum search for dense and sparse graphs.…”
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Thalassaemia detection using CBR Algorithm via mobile device by Nur Faezah, Omar

“…The results show that the Case-Based Reasoning model has a great potential to be implemented in diagnosing Thalassaemia cases.…”
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Implementation and Evaluation of Large Rsa Encryption and Decryption Keys For Internet Security by H. Belgassem, Seddeq

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PID control system implementation in embedded system for DC motor speed control by Ariff, Che Mohd Noor

“…PID algorithm that has been implemented in MCU inside the DC motor controller module system can eliminate the steady state error and overshoot problem including settling time. …”
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Faculty timetabling using genetic algorithm by Liong, Boon Yaun

“…Faculty Timetabling using Genetic Algorithm (FTGA) is an application that generate optimum timetable for faculty.The target user of this application is faculty staff who responsible in generate timetable.The problem statement of the project is many clashing exist in the timetable.Faculty staff needs to solve the clashing manually.This will waste time and it is a problem for staff to solve the clashing.By implement GA,clashing will be reduced.The objective of the project is to develop aprototype in scheduling application for generates an optimum timetable for a faculty.Genetic algorithm will be implemented.The scope of FTGA is Faculty of Computer Systems & Software Engineering (FCSSE).The methodology use in this project is prototype model.The testing result show 95 out of 100 test cases achieved the maximum fitness value which means there is no clashing in the timetable.The maximum generation is set to 15 generation.Population for each generation is 3 populations.Percentage of FTGA solve the problem is 95%.…”
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Magnetic Optimization Algorithm Approach For Travelling Salesman Problem by Ismail, Mohd Muzafar, Zakaria, Muhammad Iqbal, Zainal Abidin, Amar Faiz, Mad Juliani, Juwita, Lit, Asrani, Mirjalili, Seyedali, Nordin, Nur Anis, Mohamed Saaid, Muhammad Faiz

“…In this implementation, each magnetic agent or particle in Magnetic Optimization Algorithm represents a candidate solution of the Travelling Salesman Problem. …”
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Hybrid evolutionary optimization algorithms: A case study in manufacturing industry by Vasant, P.

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Optimisation of energy efficient Assembly Sequence Planning using Moth-Flame Optimisation alghorithm by Muhammad Arif, Abdullah

“…A computational experiment was performed by using the six test problems from the literature. Furthermore, a case study was conducted to validate the proposed EE-ASP model and the performance of the optimisation algorithms. …”
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