An ant colony approach to operation sequencing optimization in process planning

2016 ◽  
Vol 231 (3) ◽  
pp. 470-489 ◽  
Author(s):  
Quanwei Hu ◽  
Lihong Qiao ◽  
Guanwei Peng
Keyword(s):  
Ant Colony Optimization ◽  
Process Planning ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm ◽  
Operation Sequencing ◽  
Computer Aided Process Planning ◽  
Process Plans ◽  
Computer Aided

Computer-aided process planning is an important component for linking design and manufacturing in computer-aided design/computer-aided process planning/computer-aided manufacturing integrated manufacturing systems. Operation sequencing in computer-aided process planning is one of the most essential tasks. To solve the problem and acquire optimal process plans, operation sequencing is modeled as a combinatorial optimization problem with various constraints, and a novel modified ant colony optimization algorithm is developed to solve it. To ensure the feasibility of process plans, constrained relationships considered among operations are classified into two categories called precedence constraint relationships and clustering constraint relationships. Operation precedence graph based on constrained relationships is formed to get visual representation. To ensure good manufacturing economy, in the mathematical model for optimization, total weighted production cost or weighted resource transformation time related to machine changes, setup changes, tool changes, machines and tools is utilized as the evaluation criterion. To avoid local optimum and enhance global search ability, adaptive updating method and local search mechanism are embedded into the optimization algorithm. Case studies of three parts are carried out to demonstrate the feasibility and robustness of the modified ant colony optimization algorithm, and some comparisons between the modified ant colony optimization algorithm and previous genetic algorithm, simulated annealing algorithm, tabu search and particle swarm optimization algorithm are discussed. The results show that the modified ant colony optimization algorithm performs well in the operation sequencing problem.

scholarly journals An Improved Ant Colony Optimization Approach for Optimization of Process Planning

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
JinFeng Wang ◽  
XiaoLiang Fan ◽  
Haimin Ding
Keyword(s):  
Ant Colony Optimization ◽  
Process Planning ◽  
Weighted Graph ◽  
Ant Colony ◽  
Production Costs ◽  
Planning Problem ◽  
Total Production ◽  
Simple Method ◽  
Computer Aided Process Planning ◽  
Computer Aided

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

Path Planning of Robot Based on Ant Colony Optimization Algorithm

2014 ◽  
Vol 614 ◽  
pp. 199-202 ◽  
Author(s):  
Bao Ming Shan ◽  
De Xiang Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Robot Path Planning ◽  
Ant Colony Optimization Algorithm ◽  
Simulation Results ◽  
Robot Path

This paper presents a method for robot path planning based on ant colony optimization algorithm, in order to resolve the weakness of ant colony algorithm such as slow convergence rate and easy to fall into local optimum and traps. This method uses anti-potential field to make the robot escape from them smoothly, and at the end of each cycle, uses the way of judge first and then hybridization to optimize the algorithm. Finally, the simulation results show that the performance of the algorithm has been improved, and proved that the optimization algorithm is valid and feasible.

scholarly journals Optimization of operation sequencing based on GA-Jaya algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012075
Author(s):  
Kai Zheng ◽  
Rui Zhang ◽  
Zhen-Wei Zhu ◽  
Hua-Dong Zhao
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Ant Colony ◽  
Jaya Algorithm ◽  
Ant Colony Optimization Algorithm ◽  
Operation Sequencing ◽  
Swarm Optimization

Abstract To solve the operation sequencing problem in CAPP that is a difficult problem, combining the idea of genetic algorithm, an GA-Jaya algorithm is proposed to minimize the total cost. In the GA-Jaya, the population is initialized according to the procedure priority adjacency matrix which makes the population all meet the process priority relationship. Mutation iteration operator and two kinds of crossover iteration operator are designed for process sequence and processing resource evolution. The GA-Jaya algorithm is applied to a typical case, and compared with the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm. The results show that the average quality of the solution obtained by the GA-Jaya algorithm is better than the existing genetic algorithm, particle swarm optimization algorithm and ant colony optimization algorithm.

Improved Ant Colony Algorithm Based on PSO and its Application on Cloud Computing Resource Scheduling

2014 ◽  
Vol 989-994 ◽  
pp. 2192-2195 ◽  
Author(s):  
Hai Yang
Keyword(s):  
Cloud Computing ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Simulation Experiment ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Ant Colony ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

This paper introduces PSO algorithm into ant colony optimization algorithm so that an improved ant colony optimization algorithm named ACA-PSO is proposed. The ACA-PSO algorithm can get more effective optimal solutions by using PSO algorithm to do crossover operation and mutation operation so as to avoid trapping in local optimum. Finally, the simulation experiment reflects that the ACA-PSO algorithm speeds the convergence up which is more suitable for resource scheduling in cloud computing.

A Novel Combinatorial Ant Colony Optimization Algorithm with Detection Zone Rule

2014 ◽  
Vol 568-570 ◽  
pp. 1511-1515 ◽  
Author(s):  
Ya Nan Bai ◽  
Yong Chang Shi ◽  
Xiao Yu Shi
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony System ◽  
Local Optimum ◽  
Ant System ◽  
Ant Colony Optimization Algorithm ◽  
Detection Zone ◽  
Global Rate ◽  
Artificial Ants

For solving the Ant Colony System own inherent defects, this paper proposes a novel combinatorial ant Colony Optimization algorithm with detection zone rule. In the proposed algorithm, the pheromone and the search path are modified dynamically. By using the detection method, the artificial ants are detected automatically per m iterations during the detection zone. When the ant colony falls into the local optimum, the variable will be adaptive modified by the algorithm. Meanwhile, for improving the search abilities of artificial ants, it changes the global rate of pheromone evaporation and the maximum and minimum of pheromone, respectively. The performance of the novel algorithm is conducted, and the comparison among the original Ant System (AS), Ant Colony System (ACS) and proposed algorithm is shown. The experiment result demonstrated that the CACOD has a better performance than ACS in term of the capability of search and ability of restrain stagnation.

Adaptive cooperative ant colony optimization algorithm based on reinforcement incentive and its application

2021 ◽  
pp. 1-10
Author(s):  
Weiwei Yu ◽  
Chengwang Xie ◽  
Chao Deng
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Information Exchange ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Neighborhood Search ◽  
Local Optimum ◽  
Ant Colony Optimization Algorithm

Ant colony algorithm has great advantages in solving some NP complete problems, but it also has some problems such as slow search speed, low convergence accuracy and easy to fall into local optimum. In order to balance the contradiction between the convergence accuracy and the convergence speed of ant colony algorithm, this paper first proposes an ant colony algorithm (RIACO) based on the reinforcement excitation theory of Burrus Frederic Skinner. In this algorithm, pheromone is stimulated and its volatilization coefficient is adjusted adaptively according to the iteration times, thus the speed of ant colony search is accelerated. Secondly, based on the characteristics of real ant colony classification and division of labor, this paper proposes an ant colony algorithm based on labor division and cooperation (LCACO). The algorithm divides the ant colony into two different types of ant colony for information exchange and improves the state transition probability formula, so that the two ant colonies can search the optimal path cooperatively, so as to improve the precision of ant colony search. Finally, combining the two improved ant colony algorithms, this paper proposes an adaptive cooperative ant colony optimization algorithm based on reinforcement incentive (SMCAACO). A multi constrained vehicle routing problem (MCVRP) is compared with the classical tabu search algorithm (TS), variable neighborhood search algorithm (VNS) and basic ant colony algorithm (ACO). The experimental results show that, in solving the mcvrp problem, the algorithm proposed in this paper not only has a good performance in the solution results, but also achieves a good balance between the convergence speed and the convergence accuracy.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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