Search Results - optimal ((((((bees algorithm) OR (bat algorithm))) OR (based algorithm))) OR (path algorithm))

BAT-BP: A new BAT based back-propagation algorithm for efficient data classification by Mohd. Nawi, Nazri, M. Z., Rehman, Hafifi, Nurfarian, Khan, Abdullah, Siming, Insaf Ali

“…The performance of the proposed Bat-BP algorithm is then compared with Artificial Bee Colony using BPNN (ABC-BP), Artificial Bee Colony using Levenberg-Marquardt (ABC-LM) and BPNN algorithm. …”
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HEURISTIC OPTIMIZATION OF BAT ALGORITHM FOR HETEROGENEOUS SWARMS USING PERCEPTION by Kappagantula, S., Vojjala, S., Iyer, A.A., Velidi, G., Emani, S., Vandrangi, S.K.

“…Swarm Intelligence is inspired by naturally occurring swarm systems suchas Ant Colony, Bees Hive, or Bats. The Bat Algorithm is a population-based meta-heuristic algorithm for solving continuous optimization problems. …”
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Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management by Julius, Beneoluchi Odili, M. N. M., Kahar, Noraziah, Ahmad, M., Zarina, Riaz, Ul Haq

“…After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. …”
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Comfort and energy consumption optimization in smart homes using bat algorithm with inertia weight by Mohamad Razwan, Abdul Malek, Nor Azlina, Ab. Aziz, Alelyani, Salem, Mohana, Mohamed, Farah Nur Arina, Baharudin, Zuwairie, Ibrahim

“…Moreover, the comfort level achieved by BA with exponential inertia weight is found to be better than previously reported works using firefly algorithm, genetic algorithm, ant colony optimization, and artificial bee colony algorithm. …”
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Review of Multi-Objective Swarm Intelligence Optimization Algorithms by Yasear, Shaymah Akram, Ku Mahamud, Ku Ruhana

“…The MOO approaches include scalarization, Pareto dominance, decomposition and indicator-based. In this paper, the status of MOO research and state-of-the-art MOSI algorithms namely, multi-objective particle swarm, artificial bee colony, firefly algorithm, bat algorithm, gravitational search algorithm, grey wolf optimizer, bacterial foraging and moth-flame optimization algorithms have been reviewed. …”
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The Hybrid of WOABAT-IFDO Optimization Algorithm and Its Application in Crowd Evacuation Simulation by Hamizan, Sharbini, Roselina, Sallehuddin, Habibollah, Haron

“…This paper proposes a new hybrid of nature inspired optimization algorithm (IFDO-WOABAT) based on the latest optimization algorithm namely Improved Fitness Dependent Optimization (IFDO) with Whale-Bat Optimization algorithm (WOABAT). …”
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Flood Routing in River Reaches Using a Three-Parameter Muskingum Model Coupled with an Improved Bat Algorithm by Farzin, Saeed, Singh, Vijay, Karami, Hojat, Farahani, Nazanin, Ehteram, Mohammad, Kisi, Ozgur, Allawi, Mohammed Falah, Mohd, Nuruol Syuhadaa, El-Shafie, Ahmed

“…The present study attempted to develop a three-parameter Muskingum model considering lateral flow for flood routing, coupling with a new optimization algorithm namely, Improved Bat Algorithm (IBA). …”
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A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer by Alsewari, Abdul Rahman Ahmed, Sinan, Q. Salih

“…In this research, a novel swarm-based metaheuristic algorithm which depends on the behavior of nomadic people was developed, it is called ‘‘Nomadic People Optimizer (NPO)’’. …”
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Acoustic emission partial discharge localization in oil based on artificial bee colony by Lim, Zhi Yang, Azis, Norhafiz, Mohd Hashim, Ahmad Hafiz, Mohd Radzi, Mohd Amran, Norsahperi, Nor Mohd Haziq, Mohd Ariffin, Azrul

“…Comparisons with the genetic algorithm (GA), particle swarm optimization (PSO) and bat algorithm (BA) revealed that the distance error, maximum deviation and computation time for AE PD localization based on ABC are the lowest. …”
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Optimization of drilling path using the bees algorithm by Kamaruddin, Shafie, Rosdi, Mohamad Naqiuddin, Sukindar, Nor Aiman

“…This study uses the Bees Algorithm to find the best sequence of drilling holes (minimum total path length) and the results found are compared with the result of other algorithms. …”
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Optimal path planning algorithm for swarm robots using bat algorithm with mutation (bam) by Lim, Pei Yee

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Application of the Bees Algorithm to find optimal drill path sequence by Zainal Abidin, Muhammad Harith, Kamaruddin, Shafie, Adam Malek, Afiqah, Sukindar, Nor Aiman

“…The main finding of the study is that the Bees Algorithm found optimal drill path length and minimum machining time comparable to the results of the other algorithms for the 5 × 5, 7 × 7 and 9 × 9 problems. …”
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An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP by Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, Angela Amphawan

“…Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. …”
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Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm by Muhammad Syahril Mohamad Sainal

“…Meta-heuristic algorithms are mostly used to solve this problem. For example, Genetic Algorithm with Particle Swarm Optimization, Genetic Algorithm with Artificial Bee Colony Algorithms (ABC) and Genetic Algorithm with Ant Colony Optimization Algorithms. …”
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Solving large-scale problems using multi-swarm particle swarm approach by Salih, Sinan Q., Alsewari, Abdulrahman A.

“…The results showed that the proposed PSO algorithm outperformed the other algorithms in terms of the optimal solutions and the convergence.…”
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A novel swarm-based optimisation algorithm inspired by artificial neural glial network for autonomous robots by Ismail, Amelia Ritahani, Tumian, Afidalina

“…Therefore this research proposed a novel model for artificial neuro-glial networks and swarm-inspired algorithm for autonomous robots’ communication. Artificial neuro-glial networks is proposed to be combined in the swarm-based communication algorithm to provide a human-like model for the robot's communication and optimization.…”
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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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Artificial Bee Colony for Minimizing the Energy Consumption in Mobile Ad Hoc Network by Tareq, M., Abed, S.A., Sundararajan, E.A.

“…The aim of this paper is to find the best possible route from the source to the destination based on a method inspired by the searching behaviour of bee colonies, i.e. artificial bee colony (ABC) algorithm. …”
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A fuzzy adaptive teaching learning-based optimization strategy for generating mixed strength t-way test suites by Din, Fakhrud

“…Many test data generation strategies based on meta-heuristic algorithms such as Simulated Annealing (SA), Tabu Search (TS), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Harmony Search (HS), Cuckoo Search (CS), Bat Algorithm (BA) and Bees Algorithm have been developed in recent years. …”
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Gravitational search – bat algorithm for solving single and bi-objective of non-linear functions by Abbas, Iraq Tareq

“…Firstly, a new strategy based on a combined method (i.e. single-objective Gravitational Search (GSA) with Bat Algorithm (BAT) (SOGS-BAT)) algorithm is proposed in which relies on the closed interval between 0 and 1 to avoid falling into local search. …”
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