scholarly journals Penyelesaian Masalah TSP Pada Rute Kunjungan ATM Dengan Pendekatan Heuristik (Tabu Search)

2017 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Jhon Pontas Simbolon ◽  
Muhammad Zarlis
Keyword(s):  
Tabu Search ◽  
Minimum Distance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Search Process ◽  
Local Optimum ◽  
Heuristic Methods ◽  
Exact Methods

Determination of optimum route is a problem that can be found in a variety of activities. Principal of the problem is how to organize the trip so the distance is the minimum distance that the optimum is best found on a map or graph. There are many algorithms available to solve them. Algorithm is divided into two parts, the exact methods and heuristic methods. Heuristic method is considered the best method because it can work quickly. Tabu search is a heuristic method that is often used in solving optimization problems. The algorithm works by improving a solution by using memory to avoid that the search process does not get stuck at a local optimum value by rejecting new solutions that may be in memory (taboo) so that the new solution will be more dispersed. The author will implement a tabu search algorithm to provide a better alternative solution to solve the problems of the effectiveness of the distribution of charging money at the ATM machine.

scholarly journals TABU SEARCH APPROACH FOR TYPE 2 PROBLEMS OF ASSEMBLY LINE BALACING

2011 ◽  
Vol 14 (2) ◽  
pp. 22-28
Author(s):  
Hung Vo Duong
Keyword(s):  
Problem Solving ◽  
Tabu Search ◽  
Assembly Line ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Tabu Search Algorithm ◽  
Combinatorial Optimization Problems

In this research, Tabu search algorithm, a heuristic method for solving combinatorial optimization problems, has been applied for type 2 problems of assembly line balancing. For type 2 problems, two methodologies are developed for problem solving. Method 1 is direct solving for type 2 problems, and method 2 gives solving through type 1 problems. As such, Tabu search algorithm for type 1 problem is employed for problem solving at second stage. The success of this research points out empty workstations (unnecessary) to reduce investment cost and operational costs. Moreover, the range of cycle time and number of workststions are provided for selection.

Determination of Optimal Public Transportation Routes Using Firefly and Tabu Search Algorithms

2020 ◽  
Vol 4 (5) ◽  
pp. 884-891
Author(s):  
Salwa Salsabila Mansur ◽  
Sri Widowati ◽  
Mahmud Imrona
Keyword(s):  
Tabu Search ◽  
Public Transportation ◽  
Traffic Congestion ◽  
Search Algorithm ◽  
Urban Transportation ◽  
The Public ◽  
Comparison Results ◽  
Congestion Problems ◽  
Transportation Routes

Traffic congestion problems generally caused by the increasing use of private vehicles and public transportations. In order to overcome the situation, the optimization of public transportation’s route is required particularly the urban transportation. In this research, the performance analysis of Firefly and Tabu Search algorithm is conducted to optimize eleven public transportation’s routes in Bandung. This optimization aims to increase the dispersion of public transportation’s route by expanding the scope of route that are crossed by public transportation so that it can reach the entire Bandung city and increase the driver’s income by providing the passengers easier access to public transportations in order to get to their destinations. The optimal route is represented by the route with most roads and highest number of incomes. In this research, the comparison results between the reference route and the public transportation’s optimized route increasing the dispersion of public transportation’s route to 60,58% and increasing the driver’s income to 20,03%.

Determination of wheat moisture using terahertz spectroscopy combined with tabu search algorithm

2021 ◽  
Author(s):  
Yin Shen ◽  
Chunjiang Zhao ◽  
Bin Li ◽  
Guanglin Li ◽  
Yanxin Yin ◽  
...  
Keyword(s):  
Tabu Search ◽  
Moisture Content ◽  
Terahertz Spectroscopy ◽  
Search Algorithm ◽  
Grain Storage ◽  
Tabu Search Algorithm ◽  
Wheat Grains

The detection of wheat moisture content plays a key role before grain storage and classification. The harvested wheat grains were taken as samples in the current research. A total of...

scholarly journals The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)

2020 ◽  
Vol 10 (23) ◽  
pp. 8505
Author(s):  
Alireza Vafaeinejad ◽  
Samira Bolouri ◽  
Ali Asghar Alesheikh ◽  
Mahdi Panahi ◽  
Chang-Wook Lee
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Unified Approach ◽  
Exact Methods ◽  
Median Problem ◽  
Geospatial Information ◽  
Location Allocation ◽  
Ordered Median ◽  
Allocation Problems ◽  
Fire Stations

The Vector Assignment Ordered Median Problem (VAOMP) is a new unified approach for location-allocation problems, which are one of the most important forms of applied analysis in GIS (Geospatial Information System). Solving location-allocation problems with exact methods is difficult and time-consuming, especially when the number of objectives and criteria increases. One of the most important criteria in location-allocation problems is the capacity of facilities. Firstly, this study develops a new VAOMP approach by including capacity as a criterion, resulting in a new model known as VAOCMP (Vector Assignment Ordered Capacitated Median Problem). Then secondly, the results of applying VAOMP, in scenario 1, and VAOCMP, in scenario 2, for the location-allocation of fire stations in Tehran, with the objective of minimizing the arrival time of fire engines to an incident site to no more than 5 min, are examined using both the Tabu Search and Simulated Annealing algorithms in GIS. The results of scenario 1 show that 52,840 demands were unable to be served with 10 existing stations. In scenario 2, given that each facility could not accept demand above its capacity, the number of demands without service increased to 59,080, revealing that the number of stations in the study area is insufficient. Adding 35 candidate stations and performing relocation-reallocation revealed that at least three other stations are needed for optimal service. Thirdly, and finally, the VAOMP and VAOCMP were implemented in a modest size problem. The implementation results for both algorithms showed that the Tabu Search algorithm performed more effectively.

Modified Firefly Algorithm With Chaos Theory for Feature Selection

2019 ◽  
Vol 10 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Sujata Dash ◽  
Ruppa Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Feature Selection ◽  
Firefly Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Heuristic Method ◽  
High Dimensional ◽  
Support Vector ◽  
Combinatorial Optimization Problems ◽  
Meta Search ◽  
Combined Algorithm

Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts.

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

Reactive Power Compensation of the Distribution Power System with Distributed Generation Using Improved Tabu Search Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2503-2508
Author(s):  
Xiang Lei ◽  
Yan Li ◽  
Shao Rong Wang ◽  
Hong Zhao ◽  
Fen Zhou ◽  
...  
Keyword(s):  
Tabu Search ◽  
Distributed Generation ◽  
Power Loss ◽  
Cost Allocation ◽  
Reactive Power ◽  
Search Algorithm ◽  
Test System ◽  
Local Optimum ◽  
Tabu Search Algorithm ◽  
Optimal Position

Taking account of the mutual impacts of distributed generation and reactive power, to determine the optimal position and capacity of the compensation device to be installed, the paper proposed an improved Tabu search algorithm for reactive power optimization. The voltage quality is considered of the model using minimum network active power loss as objective Function. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation. On the basis of general Tabu search algorithm, the algorithm used memory guidance search strategy to focus on searching for a local optimum value, avoid a global search blindness. To deal with the neighborhood solution set properly and save algorithm storage space , some corresponding improvements are made, thus, it is easily to stop the iteration of partial optimization and it is more probable to achieve the global optimization by use of the improved algorithm. Simulations are carried out on standard IEEE 33 test system and results are presented.

scholarly journals SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems.  This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.  

scholarly journals A Learning Sparrow Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Chengtian Ouyang ◽  
Donglin Zhu ◽  
Fengqi Wang
Keyword(s):  
Path Planning ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Method ◽  
Search Process ◽  
Local Optimum ◽  
Test Function ◽  
Intelligent Optimization Algorithms ◽  
Robot Path

This paper solves the drawbacks of traditional intelligent optimization algorithms relying on 0 and has good results on CEC 2017 and benchmark functions, which effectively improve the problem of algorithms falling into local optimality. The sparrow search algorithm (SSA) has significant optimization performance, but still has the problem of large randomness and is easy to fall into the local optimum. For this reason, this paper proposes a learning sparrow search algorithm, which introduces the lens reverse learning strategy in the discoverer stage. The random reverse learning strategy increases the diversity of the population and makes the search method more flexible. In the follower stage, an improved sine and cosine guidance mechanism is introduced to make the search method of the discoverer more detailed. Finally, a differential-based local search is proposed. The strategy is used to update the optimal solution obtained each time to prevent the omission of high-quality solutions in the search process. LSSA is compared with CSSA, ISSA, SSA, BSO, GWO, and PSO in 12 benchmark functions to verify the feasibility of the algorithm. Furthermore, to further verify the effectiveness and practicability of the algorithm, LSSA is compared with MSSCS, CSsin, and FA-CL in CEC 2017 test function. The simulation results show that LSSA has good universality. Finally, the practicability of LSSA is verified by robot path planning, and LSSA has good stability and safety in path planning.

A novel optimized cyclic part feeding system with line-integrated supermarkets

2018 ◽  
Vol 233 (3) ◽  
pp. 975-987 ◽  
Author(s):  
Binghai Zhou ◽  
Jiahui Xu
Keyword(s):  
Mixed Model ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Global Optimum ◽  
Harmony Search Algorithm ◽  
Local Optimum ◽  
Fleet Size ◽  
Plant Parts ◽  
Part Feeding

To unify the merits of traditional in-plant parts logistics alternatives such as line stocking and kitting, the concept of line-integrated supermarkets is introduced to improve the part feeding in mixed-model assembly lines. First, the highly interdependent optimization problems of assigning stations and scheduling logistics operators are described, and mathematical models are established with the aim to minimize the fleet size of logistics operators and unit part delivery time as well. Together with particular theorems and lemmas, a nested dynamic programming is presented to obtain global optimum for small-sized instances while a modified harmony search algorithm is constructed for medium- or large-sized instances. Benefit from repeatedly dividing and reconstructing the harmony memory, the computation speed is significantly enhanced. Meanwhile, crossover and mutation operations effectively improve the diversity of solutions to overcome deficiencies such as limited search depth and tendencies to trapping into local optimum. Finally, experimental results validate that the proposed algorithm is of competitive performance in effectiveness and efficiency compared to some other basic or modified meta-heuristics.

Sign in / Sign up

Export Citation Format

Share Document