Search Results - (( problem using random algorithm ) OR ( java implementation path algorithm ))

Heavy Transportation Shortest Route using Dijkstra’s algorithm (HETRO) / Nurul Aqilah Ahmad Nezer by Ahmad Nezer, Nurul Aqilah

“…The development tools used in developing this project is NetBeans by using Java for the implementation of the coding. The methodology that used for developing this system is the Dijkstra’s algorithm. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

“…BST inserts the nodes in the way that the Dijkstra’s can find the empty parking in fastest way. Dijkstra’s algorithm initials the paths to finding the shortest path while ACO optimizes the paths. …”
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Path planning for unmanned aerial vehicle (UAV) using rotated accelerated method in static outdoor environment by Shaliza Hayati A. Wahab, Nordin Saad, Azali Saudi, Ali Chekima

“…In this study, a fast iterative method known as Rotated Successive Over-Relaxation (RSOR) is introduced. The algorithm is implemented in a self-developed 2D Java tool, UAV Planner. …”
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Smart appointment organizer for mobile application / Mohd Syafiq Adam by Adam, Mohd Syafiq

“…The main component of this prototype is the use of Dijkstra algorithm to compute the shortest path from source of appointment to the 6 points of destinations within UiTM Shah Alam. …”
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African Buffalo Optimization and the Randomized Insertion Algorithm for the Asymmetric Travelling Salesman’s Problems by Odili, Julius Beneoluchi, M. N. M., Kahar, Noraziah, Ahmad, Odili, Esther Abiodun

“…This paper presents a comparative study of the African Buffalo Optimization algorithm and the Randomized Insertion Algorithm to solving the asymmetric Travelling Salesman’s Problem with the overall objective of determining a better method to solving the asymmetric Travelling Salesman’s Problem instances. …”
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A Comparative Study of African Buffalo Optimization and Randomized Insertion Algorithm for Asymmetric Travelling Salesman's Problem by Odili, Julius Beneoluchi, M. N. M., Kahar, Shahid, Anwar, Azrag, M. A. K.

“…In this study, a comparative study of the African Buffalo Optimization algorithm and the Randomized Insertion Algorithm to solving the asymmetric Travelling Salesman's Problem is made with the aim of ascertaining a better method to solving the asymmetric Travelling Salesman's Problem instances. …”
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Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

“…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems by Nor Shuhada, Ibrahim

“…This PhD thesis introduces an extended version of MABSA aimed at addressing constrained multi objective optimization problems by incorporating innovative random approaches, focusing to solves reservoir optimization problems. …”
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The Anglerfish algorithm: a derivation of randomized incremental construction technique for solving the traveling salesman problem by Pook, Mei Foong, Ramlan, Effirul Ikhwan

“…Benchmarking is conducted using the traveling salesman problem. The results are comparable with the results of advanced metaheuristic algorithms. …”
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Combining approximation algorithm with genetic algorithm at the initial population for NP-complete problem by Razip, H., Zakaria, M.N.

“…We tested this approach by sampling the populations for some Set Covering Problems from OR Library using the randomized rounding method (AAR) and compared them with that sampled using a randomized heuristics (R) in terms of redundancy rate, diversity and closeness to the optimal solution (OPT). …”
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Comparative Analysis of Low Discrepancy Sequence-Based Initialization Approaches Using Population-Based Algorithms for Solving the Global Optimization Problems by Waqas Haider Bangyal, Kashif Nisar, Ag. Asri Bin Ag. Ibrahim, Muhammad Reazul Haque, Joel J. P. C. Rodrigues, Danda B. Rawat

“…Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. …”
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Node placement optimization using extended virtual force and cuckoo search algorithm in wireless sensor network by Puteri Azwa, Ahmad

“…The implementation of the EVFCS improved the problems of initial random deployment.…”
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Improved stochastic gradient descent algorithm with mean-gradient adaptive stepsize for solving large-scale optimization problems by Zulkifli, Munierah, Abd Rahmin, Nor Aliza, Wah, June Leong

“…Stochastic gradient descent (SGD) is one of the most common algorithms used in solving large unconstrained 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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A comparative performance analysis of computational intelligence techniques to solve the asymmetric travelling salesman problem by Odili, Julius Beneoluchi, Noraziah, Ahmad, Zarina, M.

“…The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. …”
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Comparison between Genetic Algorithm and Prey-Predator Algorithm. by Ong, Hong Choon

“…The use of metaheuristic algorithms to different problems becomes very common after the introduction of genetic algorithm in 1975. …”
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Hybrid multiobjective genetic algorithm for integrated dynamic scheduling and routing of jobs and automated guided vehicles in flexible manufacturing systems by Umar, Umar Ali

“…The comparison of the result of the developed integrated algorithm using two benchmark FMS scheduling algorithms datasets is conducted. …”
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The normalized random map of gradient for generating multifocus image fusion by Ismail, ., Kamarul Hawari, Ghazali

“…In order to handle this problem, the proposed method a concise algorithm which is able to generate an accurate fused image without using a complicated mathematical equation and tough algorithm. …”
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Solving Assembly Line Balancing Problem Using Genetic Algorithm With Heuristics-Treated Initial Population by Chong, Kuan Eng, Omar, Mohamed K, Abu Bakar, Nooh

“…Although genetic algorithm (GA) has been widely used to address assembly line balancing problems (ALBP), not much attention has been given to the population initialization procedure. …”
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A Comparative Study on three Component Selection Mechanisms for Hyper-Heuristics in Expensive Optimization by Jia Hui Ong, Jason Teo

“…Numerous studies in optimization problems often lead to tailoring a specific algorithm to adapt to the problem instances, especially in expensive optimization problems. …”
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