Search Results - (( evolution optimization bat algorithm ) OR ( variable objective evolutionary algorithm ))

Multi-Swarm bat algorithm by Taha A.M., Chen S.-D., Mustapha A.

“…In this study a new Bat Algorithm (BA) based on multi-swarm technique called the Multi-Swarm Bat Algorithm (MSBA) is proposed to address the problem of premature convergence phenomenon. …”

Fuzzy optimization with multi-objective evolutionary algorithms: A case study by P., Vasant, F., Jimenez, G., Sanchez

“…On the other hand, an ad hoc Pareto-based multi-objective evolutionary algorithm to capture multiple non dominated solutions in a single run of the algorithm is described. …”
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A multi-objective evolutionary approach for fuzzy optimization in production planning by P., Vasant, F., Jimenez, G., Sanchez, J.L., Verdegay

“…Results obtained have been compared with the well-known multi-objective evolutionary algorithm NSGA-II. © 2006 IEEE. …”
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A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

“…These models are developed by integrating multilayer perceptron neural network and evolutionary optimization techniques. Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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Evolutionary and population dynamics of 3 parents differential evolution (3PDE) using self-adaptive tuning methodologies by Teng, Nga Sing, Teo, Jason Tze Wi

“…In this paper, 50 repeated evolutionary runs for each of 20 well-known benchmarks were carried out to test the proposed algorithms against the original 4-parents DE algorithm. …”
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Quality of service and energy efficient aware (QEEA) scheduling algorithm for long term evolution (LTE) network / Nurulanis Mohd Yusoff by Mohd Yusoff, Nurulanis

“…Basically, the QEEA is based on the Time Domain (TD) and Frequency Domain (FD) scheduling where it is dependent on the QoS requirements to allocate resources. The proposed algorithm is compared against other scheduling algorithms, namely, the Channel and QoS Aware (CQA), Priority Set Scheduler (PSS), Proportional Fair (PF), Maximum Throughput (MT) and Blind Average Throughput (BAT). …”
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Hybrid genetic algorithms and line search method for industrial production planning with non-linear fitness function by Vasant, Pandian

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Automated calibration of baseline model for energy conservation using multi-objective Evolutionary Programming (EP) / Ahmad Amiruddin Mohammad Aris by Mohammad Aris, Ahmad Amiruddin

“…This thesis presents multi-objective optimization approach in developing baseline energy using multi-objective Evolutionary Programming (EP). …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail by Ismail, Nor Laili

“…Subsequently, a new multi-objective optimisation technique named Multi-Objective Hybrid Evolutionary-Barnacles Mating Optimisation (MOHEBMO) was developed to solve the minimization problems involving the total generation cost and total emission of harmful gasses in a multi-objective mode. …”
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A Hybrid of Optimization Method for Multi-Objective Constraint Optimization of Biochemical System Production by Mohd Arfian, Ismail, Safaai, Deris, Mohd Saberi, Mohamad, Mohd Adham, Isa, Afnizanfaizal, Abdullah, Muhammad Akmal, Remli, Mohi-Aldeen, Shayma Mustafa

“…The proposed method combines Newton method, Strength Pareto Evolutionary Algorithm (SPEA) and Cooperative Co-evolutionary Algorithm (CCA). …”
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Economic power dispatch solutions incorporating stochastic wind power generators by moth flow optimizer by Alam, Mohammad Khurshed, Mohd Herwan, Sulaiman, Sayem, Md. Shaoran, Khan, Rahat

“…The introduction of renewable energy sources into the smart grid of the present enables the emergence of novel optimization problems with an abundance of new variables. This study's primary purpose is to apply state-of-the-art variations of the differential evolution (DE) algorithm for single-objective optimization and selected evolutionary algorithms for multi-objective optimization issues in power systems. …”
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Systematic design of chemical reactors with multiple stages via multi-objective optimization approach by Mohd Fuad, Mohd Nazri, Hussain, Mohd Azlan

“…Following the identification of path-dependent design variables, several (possibly conflicting) design objectives will be selected and solutions of the corresponding problem will be generated from multi-objective optimization algorithm. …”
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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization by Mohd Zain, Mohamad Zihin, Kanesan, Jeevan, Chuah, Joon Huang, Dhanapal, Saroja, Kendall, Graham

“…M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). …”
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Optimum MV Feeder Routing and Substation siting and rating in Distribution Network by Hasan, Ihsan Jabbar, Ab Ghani, Mohd Ruddin, Gan, Chin Kim

“…This paper proposes an evolutionary algorithm to determine the optimum distribution substation placement and sizing by using the particle swarm optimization algorithm and optimum feeder routing using modified minimum spanning tree algorithm. …”
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A swarm intelligent approach for multi-objective optimization of compact heat exchangers by Yousefi M., Yousefi M., Martins Ferreira R.P., Darus A.N.

Multi-Objective Multi-Exemplar Particle Swarm Optimization Algorithm with Local Awareness by Noori, Mustafa Sabah, Sahbudin, Ratna K.Z., Sali, Aduwati, Hashim, Fazirulhisyam

“…Many machine learning algorithms excel at handling problems with conflicting objectives. …”
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A high-performance democratic political algorithm for solving multi-objective optimal power flow problem by Ahmadipour, Masoud, Ali, Zaipatimah, Othman, Muhammad Murtadha, Bo, Rui, Javadi, Mohammad Sadegh, Ridha, Hussein Mohammed, Alrifaey, Moath

“…Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. …”
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Optimization of machining parameters in turning for different hardness using multi-objective genetic algorithm / Mimi Muzlina Mukri, Nor Atiqah Zolpaka and Sunil Pathak by Mukri, Mimi Muzlina, Zolpaka, Nor Atiqah Zolpaka, Pathak, Sunil

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Multi-objective Optimization of Biochemical System Production Using an Improve Newton Competitive Differential Evolution Method by Mohd Arfian, Ismail, Mezhuyev, Vitaliy, Safaai, Deris, Mohd Saberi, Mohamad, Shahreen, Kasim, Saedudin, Rd Rohmat

“…In addition, the multi-objective problem also needs to be considered. Due to that, this study proposed and improved a method that comprises with Newton method, differential evolution algorithm (DE) and competitive co-evolutionary algorithm(ComCA). …”
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A high-performance democratic political algorithm for solving multi-objective optimal power flow problem by Ahmadipour M., Ali Z., Othman M.M., Bo R., Javadi M.S., Ridha H.M., Alrifaey M.

“…Simulation results are analyzed and compared with two popular and commonly used multi-objective-evolutionary algorithms namely, non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) on the problem. …”