Search Results - (( evolution optimization method algorithm ) OR ( motion optimization system algorithm ))

Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

“…The proposed method is tested on 10 multi-objective benchmark problems of CEC 2009 and compared with four metaheuristics: Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), Multi-Objective Differential Evolution (MODE) and MOPSO. …”
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Investigation of the optimal sensor location and classifier for human motion classification by Anuar, Mohamed, Nur Aqilah, Othman, Hamzah, Ahmad, Mohd Hasnun Ariff, Hassan

“…Future development of wearables should look into integrating classification algorithm in the system, thus the human motion monitoring will provide a richer information and not only limited to number of steps and calories burned.…”
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Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse) by Jia, Xing Yeoh, Chuii, Khim Chong, Mohd Saberi, Mohamad, Yee, Wen Choon, Lian, En Chai, Safaai, Deris, Zuwairie, Ibrahim

“…The hybrid of Differential Evolution algorithm with Kalman Filtering and Bacterial Foraging algorithm is a novel global optimisation method implemented to obtain the best kinetic parameter value. …”
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A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising by Al-Dabbagh, Mohanad Dawood, Al-Dabbagh, Rawaa Dawoud, Raja Abdullah, Raja Syamsul Azmir, Hashim, Fazirulhisyam

“…The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. …”
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Accelerated black hole optimization algorithm with enhanced FOPID controller for omni-wheel drive mobile robot system by Basil, Noorulden, Marhoon, Hamzah M., Sahib, Dheyaaldeen Faez, Mohammed, Abdullah Fadhil, Ridha, Hussein Mohammed, Ma’arif, Alfian

“…To optimize controller performance, six different optimization algorithms are compared are Accelerated Convergence Black Hole Optimization (ACBHO), Black Hole Optimization (BHO), Aquila Optimizer (AO), Hybrid Firefly Particle Swarm Optimization (HFPSO), Enhanced JAYA (EJAYA), and Sunflower Optimizer (SFO). …”
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Differential evolution optimization algorithm based on generation systems reliability assessment integrated with wind energy by Kadhem, Athraa Ali, Abdul Wahab, Noor Izzri, Abdalla, Ahmed N.

“…This stuffy proposed a novel optimization method labeled the "Differential Evolution Optimization Algorithm" (DEOA) to assess the reliability of power generation systems (PGS). …”
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Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution by Ganesan, T., Elamvazuthi, I., Shaari, K.Z.K., Vasant, P.

“…In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). …”
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HEAT EXCHANGER NETWORK SYNTHESIS AND OPTIMIZATION BY PINCH ANALYSIS AND DIFFERENTIAL EVOLUTION METHOD by NGO , THI PHUONG THUY

“…This metadology way uses an algorithm which combines Pinch Design Method and Differential Evolution Method. …”
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Optimization Of Sliding Mode Control Using Particle Swarm Algorithm For An Electro-Hydraulic Actuator System by Rozaimi, Ghazali

“…The dynamic parts of electro-hydraulic actuator(EHA) system are widely applied in the industrial field for the process that exposed to the motion control.In order to achieve accurate motion produced by these dynamic parts,an appropriate controller will be needed.However,the EHA system is well known to be nonlinear in nature.A great challenge is carried out in the EHA system modelling and the controller development due to its nonlinear characteristic and system complexity.An appropriate controller with proper controller parameters will be needed in order to maintain or enhance the performance of the utilized controller.This paper presents the optimization on the variables of sliding mode control (SMC) by using Particle Swarm Optimization (PSO) algorithm.The control scheme is established from the derived dynamic equation which stability is proven through Lyapunov theorem.From the obtained simulation results,it can be clearly inferred that the SMC controller variables tuning through PSO algorithm performed better compared with the conventional proportionalintegral-derivative (PID) controller.…”
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A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Shamsudin, Shamsudin, Mohamaddan, Shahrol

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Optimal location and size of distributed generation to reduce power losses and improve voltage profiles using differential evolution optimization method by Hammadi, Ahmed Sahib

“…The results obtained by using the DE method were compared with those obtained by genetic algorithm (GA) method. …”
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Parameter extraction of photovoltaic module using hybrid evolutionary algorithm by Muhsen D.H., Ghazali A.B., Khatib T.

“…Algorithms; Diodes; Extraction; Iterative methods; Optimization; Parameter estimation; Parameter extraction; Photovoltaic cells; Different evolutions; Differential Evolution; Diode modeling; Electromagnetism-like algorithm; Extracting parameter; Hybrid evolutionary algorithm; Photovoltaic model; Photovoltaic modules; Evolutionary algorithms…”

Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system by Khaksar W., Hong T.S., Sahari K.S.M., Khaksar M., Torresen J.

Multi-objective optimization of two-stage thermo-electric cooler using differential evolution: MO optimization of TEC using DE by Khanh, D.V.K., Vasant, P.M., Elamvazuthi, I., Dieu, V.N.

“…In this chapter, the technical issues of two-stage TEC were discussed. After that, a new method of optimizing the dimension of TECs using differential evolution to maximize the cooling rate and coefficient of performance was proposed. …”
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A holistic review on artificial intelligence techniques for well placement optimization problem by Islam, J., Vasant, P.M., Negash, B.M., Laruccia, M.B., Myint, M., Watada, J.

“…Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. …”
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A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

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Optimization Of Fractional-Slot Permanent Magnet Synchronous Machine Using Analytical Sub-Domain Model And Differential Evolution by Mohamed, Mohd Rezal

“…From the results obtained, the Analytical Sub-Domain Differential Evolution Algorithm (ASDEA) has better optimization technique capability compared with Analytical Sub-Domain Particle Swarm Optimization (ASPSO). …”
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Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

“…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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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.

A New Co-Evolution Binary Particle Swarm Optimization With Multiple Inertia Weight Strategy For Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah

“…To examine the effectiveness of proposed method, four recent and popular feature selection methods namely BPSO, genetic algorithm (GA), binary gravitational search algorithm (BGSA) and competitive binary grey wolf optimizer (CBGWO) are used in a performance comparison. …”
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