scholarly journals Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm

2015 ◽  
Vol 5 (4) ◽  
pp. 239-245 ◽  
Author(s):  
Ahmad Fouad El-Samak ◽  
Wesam Ashour
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Affinity Propagation ◽  
Computational Time ◽  
Combinatorial Optimization Problems ◽  
Affinity Propagation Clustering ◽  
Core Idea ◽  
Computation Algorithms

Abstract Combinatorial optimization problems, such as travel salesman problem, are usually NP-hard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.

Optimization Method RasID-GA for Numerical Constrained Optimization Problems

2007 ◽  
Vol 11 (5) ◽  
pp. 469-477 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Random Search ◽  
Optimal Solution ◽  
Solution Space ◽  
Optimization Method ◽  
Penalty Functions ◽  
Objective Functions ◽  
Constrained Optimization Problems

This paper proposes Adaptive Random search with Intensification and Diversification combined with Genetic Algorithm (RasID-GA) for constrained optimization. In the previous work, we proposed RasID-GA which combines the best properties of RasID and Genetic Algorithm for unconstrained optimization problems. In general, it is very difficult to find an optimal solution for constrained optimization problems because their feasible solution space is very limited and they should consider the objective functions and constraint conditions. The conventional constrained optimization methods usually use penalty functions to solve given problems. But, it is generally recognized that the penalty function is hard to handle in terms of the balance between penalty functions and objective functions. In this paper, we propose a constrained optimization method using RasID-GA, which solves given problems without using penalty functions. The proposed method is tested and compared with Evolution Strategy with Stochastic Ranking using well-known 11 benchmark problems with constraints. From the Simulation results, RasID-GA can find an optimal solution or approximate solutions without using penalty functions.

scholarly journals A Parallel Bioinspired Algorithm for Chinese Postman Problem Based on Molecular Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhaocai Wang ◽  
Xiaoguang Bao ◽  
Tunhua Wu
Keyword(s):  
Computational Complexity ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solution Space ◽  
Research Direction ◽  
Chinese Postman Problem ◽  
Combinatorial Optimization Problems ◽  
Chinese Postman ◽  
Dna Algorithm

The Chinese postman problem is a classic resource allocation and scheduling problem, which has been widely used in practice. As a classical nondeterministic polynomial problem, finding its efficient algorithm has always been the research direction of scholars. In this paper, a new bioinspired algorithm is proposed to solve the Chinese postman problem based on molecular computation, which has the advantages of high computational efficiency, large storage capacity, and strong parallel computing ability. In the calculation, DNA chain is used to properly represent the vertex, edge, and corresponding weight, and then all possible path combinations are effectively generated through biochemical reactions. The feasible solution space is obtained by deleting the nonfeasible solution chains, and the optimal solution is solved by algorithm. Then the computational complexity and feasibility of the DNA algorithm are proved. By comparison, it is found that the computational complexity of the DNA algorithm is significantly better than that of previous algorithms. The correctness of the algorithm is verified by simulation experiments. With the maturity of biological operation technology, this algorithm has a broad application space in solving large-scale combinatorial optimization problems.

scholarly journals Genetic algorithm optimization for dynamic construction site layout planning

2018 ◽  
Vol 10 (1) ◽  
pp. 1655-1664 ◽  
Author(s):  
Panagiotis M. Farmakis ◽  
Athanasios P. Chassiakos
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Solution Space ◽  
Time Varying ◽  
Construction Site ◽  
Large Solution ◽  
Layout Planning ◽  
Combinatorial Optimization Problems ◽  
Site Layout ◽  
Construction Site Layout

AbstractThe dynamic construction site layout planning (DCSLP) problem refers to the efficient placement and relocation of temporary construction facilities within a dynamically changing construction site environment considering the characteristics of facilities and work interrelationships, the shape and topography of the construction site, and the time-varying project needs. A multi-objective dynamic optimization model is developed for this problem that considers construction and relocation costs of facilities, transportation costs of resources moving from one facility to another or to workplaces, as well as safety and environmental considerations resulting from facilities’ operations and interconnections. The latter considerations are taken into account in the form of preferences or constraints regarding the proximity or remoteness of particular facilities to other facilities or work areas. The analysis of multiple project phases and the dynamic facility relocation from phase to phase highly increases the problem size, which, even in its static form, falls within the NP (for Nondeterministic Polynomial time)-hard class of combinatorial optimization problems. For this reason, a genetic algorithm has been implemented for the solution due to its capability to robustly search within a large solution space. Several case studies and operational scenarios have been implemented through the Palisade’s Evolver software for model testing and evaluation. The results indicate satisfactory model response to time-varying input data in terms of solution quality and computation time. The model can provide decision support to site managers, allowing them to examine alternative scenarios and fine-tune optimal solutions according to their experience by introducing desirable preferences or constraints in the decision process.

scholarly journals Optimization of Network Coding Resources Based on Improved Quantum Genetic Algorithm

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 502
Author(s):  
Tianyang Liu ◽  
Qiang Sun ◽  
Huachun Zhou ◽  
Qi Wei
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Topological Structure ◽  
Rotation Angle ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Resource Optimization ◽  
Quantum Genetic Algorithm ◽  
Adjustment Mechanism ◽  
Adaptive Adjustment

The problem of network coding resource optimization with a known topological structure is NP-hard. Traditional quantum genetic algorithms have the disadvantages of slow convergence and difficulty in finding the optimal solution when dealing with this problem. To overcome these disadvantages, this paper proposes an adaptive quantum genetic algorithm based on the cooperative mutation of gene number and fitness (GNF-QGA). This GNF-QGA adopts the rotation angle adaptive adjustment mechanism. To avoid excessive illegal individuals, an illegal solution adjustment mechanism is added to the GNF-QGA. A solid demonstration was provided that the proposed algorithm has a fast convergence speed and good optimization capability when solving network coding resource optimization problems.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

Space Contraction and Partition Algorithm for Combinatorial Optimization

2011 ◽  
Vol 421 ◽  
pp. 559-563
Author(s):  
Yong Chao Gao ◽  
Li Mei Liu ◽  
Heng Qian ◽  
Ding Wang
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Solution Space ◽  
Search Space ◽  
Small Scale ◽  
Optimal Solutions ◽  
Solution Quality ◽  
Intelligent Algorithms ◽  
Combinatorial Optimization Problems

The scale and complexity of search space are important factors deciding the solving difficulty of an optimization problem. The information of solution space may lead searching to optimal solutions. Based on this, an algorithm for combinatorial optimization is proposed. This algorithm makes use of the good solutions found by intelligent algorithms, contracts the search space and partitions it into one or several optimal regions by backbones of combinatorial optimization solutions. And optimization of small-scale problems is carried out in optimal regions. Statistical analysis is not necessary before or through the solving process in this algorithm, and solution information is used to estimate the landscape of search space, which enhances the speed of solving and solution quality. The algorithm breaks a new path for solving combinatorial optimization problems, and the results of experiments also testify its efficiency.

Genetic Algorithm and Other Meta-Heuristics

2005 ◽  
pp. 144-173 ◽  
Author(s):  
Bernard K.S. Cheung
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Schema Theory ◽  
Heuristic Methods ◽  
Optimal Solutions ◽  
Corporate Globalization ◽  
Global Optimal ◽  
Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

A New Multi-Criteria Solving Procedure for Multi-Depot FSM-VRP with Time Window

2017 ◽  
Vol 4 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Lahcene Guezouli ◽  
Samir Abdelhamid
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Vehicle Routing Problem ◽  
Processing Time ◽  
Optimization Problems ◽  
Time Window ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Capacitated Vehicle

One of the most important combinatorial optimization problems is the transport problem, which has been associated with many variants such as the HVRP and dynamic problem. The authors propose in this study a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of different vehicle types (with distinct capacities and costs) and multiple available depots, that the authors call the Multi-Depot HVRPTW by respecting a set of criteria including: schedules requests from clients, the heterogeneous capacity of vehicles..., and the authors solve this problem by proposing a new scheme based on a genetic algorithm heuristics that they will specify later. Computational experiments with the benchmark test instances confirm that their approach produces acceptable quality solutions compared with previous results in similar problems in terms of generated solutions and processing time. Experimental results prove that the method of genetic algorithm heuristics is effective in solving the MDHVRPTW problem and hence has a great potential.

The Simulation Optimization for Job-Shop Scheduling Based on Plant Simulation Using Genetic Algorithm

2012 ◽  
Vol 217-219 ◽  
pp. 1444-1448
Author(s):  
Xiang Ke Tian ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Combinatorial Optimization Problems ◽  
Plant Simulation

The job-shop scheduling problem (JSP), which is one of the best-known machine scheduling problems, is among the hardest combinatorial optimization problems. In this paper, the key technology of building simulation model in Plant Simulation is researched and also the build-in genetic algorithm of optimizing module is used to optimize job-shop scheduling, which can assure the scientific decision. At last, an example is used to illustrate the optimization process of the Job-Shop scheduling problem with Plant Simulation genetic algorithm modules.

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