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Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

“…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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Stochastic process and tutorial of the African bufalo optimization by Odili, Julius Beneoluchi, Noraziah, Ahmad, Alkazemi, Basem Y., M., Zarina

“…This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. …”
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Stochastic process and tutorial of the African buffalo optimization by Odili, Julius Beneoluchi, Noraziah, Ahmad, Alkazemi, Basem, Zarina, M.

“…This paper presents the data description of the African buffalo optimization algorithm (ABO). ABO is a recently-designed optimization algorithm that is inspired by the migrant behaviour of African buffalos in the vast African landscape. …”
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Gradient-based mutation manta ray foraging optimization (gbm-mrfo) for solving constrained real-world problems by Ahmad Azwan, Abdul Razak, Ahmad Nor Kasruddin, Nasir

“…GbM-MRFO turn out to be a competitive optimization algorithm on solving constrained optimization problem of Three-bar Truss problem. …”
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T-way testing : a test case generator based on melody search algorithm by Toh, Shu Yuen

“…Next, TTT-MS will be executed through main algorithms to generate Parameters Interaction List, Parameter Values Interaction List, and finally generate final Test Cases based on Melody Search Algorithm. …”
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Novel Multi-swarm Approach for Balancing Exploration and Exploitation in Particle Swarm Optimization by Salih, Sinan Q., Alsewari, Abdulrahman A., Al-Khateeb, Bellal, Mohamad Fadli, Zolkipli

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Hybrid-discrete multi-objective particle swarm optimization for multi-objective job-shop scheduling by Anuar, Nurul Izah

“…Many real-world production scheduling problems involve the simultaneous optimization of multiple conflicting objectives that are challenging to solve without the aid of powerful optimization techniques. …”
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Improving Attentive Sequence-to-Sequence Generative-Based Chatbot Model Using Deep Neural Network Approach by Wan Solehah, Wan Ahmad

“…Hence, this research’s objective aimed to propose an optimization strategy based on Structural Modification and Optimizing Training Network for improving the lacking of accuracy of response in the chatbot application, to propose the algorithm enhancement to improve the current attention mechanism in the Attentive Sequence-to-Sequence model and the network’s training optimization of its inability to memorize the dialogue history, and lastly, to evaluate the accuracy of response of the proposed solution through data training on loss function and real data testing. …”
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Neural Network – A Black Box Model by Kuok, Kuok King, Chan, Chiu Po, Md. Rezaur, Rahman, Khairul Anwar, Mohamad Said, Chin Mei, Yun

“…A variety of metaheuristic algorithms have been used to train ANN, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO), Tabu Search (TS), and Harmony Search (HS). …”
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EMG motion pattern classification through design and optimization of neural network by Ahsan, Md. Rezwanul, Ibrahimy, Muhammad Ibn, Khalifa, Othman Omran

“…This paper illustrates the classification of EMG signals through design and optimization of Artificial Neural Network (ANN). Different types of ANN models are basically structured with many interconnected network elements which can develop pattern classification strategies based on a set of input/training data. …”
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Implementation of Symmetric Rank-One Methods for Unconstrained Optimization by Khiyaban, Farzin Modarres

“…We then examine a new scaled memoryless SR1 method based on modied secant equation for solving large-scale unconstrained optimization problems. …”
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Tasks Distribution In Driver Scheduling Using Dynamic Set Of Bandwidth In Harmony Search Algorithm With 2-Opt by Shaffiei, Zatul Alwani

“…This research was focused on driver scheduling problem for university shuttle bus (DSPUSB). Based on previous research using one of metaheuristic algorithms known as harmony search (HS), the generated schedule was still not optimum and cannot be solved maximally as there were too much repetitions of task (shift and route) occurred among drivers. …”
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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.…”
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Optimal planning and design of hybrid renewable energy system for rural healthcare facilities / Olatomiwa Lanre Joseph by Olatomiwa Lanre , Joseph

“…Then, utilization of cost-effective optimization algorithm for optimal sizing of the energy resources and other system components with accurate mathematical models for energy management of the entire hybrid system. …”
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Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems by Muhammad Sam’An, Muhammad Sam’An, Yosza Dasril, Yosza Dasril, Chandrasekar Ramasamy, Chandrasekar Ramasamy, Bujang, Nazarudin, Yahya, Nur Ifriza

“…The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. …”
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Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems by Muhammad Sam’An, Muhammad Sam’An, Yosza Dasril, Yosza Dasril, Chandrasekar Ramasamy, Chandrasekar Ramasamy, Nazarudin Bujang, Nazarudin Bujang, Yahya Nur Ifriza, Yahya Nur Ifriza

“…The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. …”
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Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems by Muhammad Sam’An, Muhammad Sam’An, Yosza Dasril, Yosza Dasril, Chandrasekar Ramasamy, Chandrasekar Ramasamy, Nazarudin Bujang, Nazarudin Bujang, Yahya Nur Ifriza, Yahya Nur Ifriza

“…The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. …”
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Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems by Muhammad Sam’An, Muhammad Sam’An, Yosza Dasril, Yosza Dasril, Chandrasekar Ramasamy, Chandrasekar Ramasamy, Nazarudin Bujang, Nazarudin Bujang, Yahya Nur Ifriza, Yahya Nur Ifriza

“…The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. …”
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Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems by Muhammad Sam’An, Muhammad Sam’An, Yosza Dasril, Yosza Dasril, Chandrasekar Ramasamy, Chandrasekar Ramasamy, Nazarudin Bujang, Nazarudin Bujang, Yahya Nur Ifriza, Yahya Nur Ifriza

“…The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. …”
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Improved Segregated Advancement (I-SA): a new method for solving full triangular fuzzy transportation problems by Muhammad Sam’An, Muhammad Sam’An, Yosza Dasri, Yosza Dasri, Chandrasekar Ramasamy, Chandrasekar Ramasamy, Nazarudin Bujang, Nazarudin Bujang, Yahya Nur Ifriza, Yahya Nur Ifriza

“…The flaw of SA is that it uses the classical algorithms which are NWC, LCM, and VAM. Therefore, we proposed the improvement based on the combination of total ratio cost matrix and total difference method by column without using classical ranking functions. …”
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