Optimal Sensor Deployment to Diagnose Large-Scale Manufacturing Systems Using a Convergence-Trajectory Controlled Ant Colony System Algorithm

2021 ◽  
Author(s):  
Rajshekhar Singhania ◽  
Chinmay Sawkar ◽  
Manoj K. Tiwari
Keyword(s):  
Manufacturing Systems ◽  
Large Scale ◽  
Optimal Solution ◽  
Ant Colony ◽  
Set Covering ◽  
Covering Problem ◽  
Sensor Deployment ◽  
Ant Colony System ◽  
Computational Budget ◽  
Acs Algorithm

Abstract In this article, the problem of optimal sensor deployment in large-scale manufacturing systems for effective process monitoring is solved using a variant of the ant colony system (ACS) algorithm to obtain an optimal number of sensors, their types, and locations for monitoring various possible faults. For this purpose, first, we define the need for optimizing sensor deployment in large-scale manufacturing processes because of the increasing application of Wireless Sensor Networks (WSNs) as an architectural framework for Machine-to-Machine (M2M) communications and Cyber-Physical Systems (CPS). Then a multi-objective formulation of optimal sensor deployment in a Single Station Multi-Step Manufacturing Process concerning sensor costs, system reliability, and stability is briefly explained. As noted earlier by several authors, the sensor deployment problem is a set covering problem. It is known that metaheuristics like genetic algorithms, variants of ant colony algorithms, etc. are not efficient in finding a near-optimal solution in less computational budget to the large-scale set covering problems. For this purpose, a Convergence Trajectory Controlled ant colony system is developed and applied on a case study of an automated assembly robot. For an effective demonstration of the convergence power of the developed algorithm, we also apply our algorithm on some large-scale benchmark datasets of the set covering problem and compare the results obtained with the ant colony system algorithm. The results obtained show that the developed algorithm can give a near-optimal solution in less computational budget than the ACS algorithm.

Logistics for the Garbage Collection through the use of Ant Colony Algorithms

2012 ◽  
pp. 33-51
Author(s):  
Julio Cesar Ponce Gallegos ◽  
Fatima Sayuri Quezada Aguilera ◽  
José Alberto Hernandez Aguilar ◽  
Christian José Correa Villalón
Keyword(s):  
Waste Management ◽  
Garbage Collection ◽  
Optimization Problems ◽  
Decision Makers ◽  
Ant Colony ◽  
Ant Colony System ◽  
Waste Collection ◽  
Combinatorial Optimization Problems ◽  
Ant Colony Algorithms ◽  
Acs Algorithm

The contribution of this chapter is to present an approach to explain the Ant Colony System applied on the Waste Collection Problem, because waste management is moving up to the concern over health and environmental impacts. These algorithms are a framework for decision makers in order to analyze and simulate various spatial waste management problems. In the last decade, metaheuristics have become increasingly popular for effectively confronting difficult combinatorial optimization problems. In the present work, an individual metaheuristic Ant Colony System (ACS) algorithm is introduced, implemented and discussed for the identification of optimal routes in the case Solid Waste collection. This algorithm is applied to a waste collection and transport system, obtaining recollection routes with the less total distance with respect to the actual route utilized and to the solution obtained by a previously developed approach.

scholarly journals Solving Set Cover and Dominating Set via Maximum Satisfiability

2020 ◽  
Vol 34 (02) ◽  
pp. 1569-1576 ◽  
Author(s):  
Zhendong Lei ◽  
Shaowei Cai
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Dominating Set ◽  
Scoring Function ◽  
Initial Solution ◽  
Set Covering ◽  
Set Cover ◽  
Covering Problem ◽  
Greedy Heuristic ◽  
Maximum Satisfiability

The Set Covering Problem (SCP) and Dominating Set Problem (DSP) are NP-hard and have many real world applications. SCP and DSP can be encoded into Maximum Satisfiability (MaxSAT) naturally and the resulting instances share a special structure. In this paper, we develop an efficient local search solver for MaxSAT instances of this kind. Our algorithm contains three phrase: construction, local search and recovery. In construction phrase, we simplify the instance by three reduction rules and construct an initial solution by a greedy heuristic. The initial solution is improved during the local search phrase, which exploits the feature of such instances in the scoring function and the variable selection heuristic. Finally, the corresponding solution of original instance is recovered in the recovery phrase. Experiment results on a broad range of large scale instances of SCP and DSP show that our algorithm significantly outperforms state of the art solvers for SCP, DSP and MaxSAT.

Bio-Inspired Algorithm for the Shortest Path According to the Maximum Time for Each Trial

2013 ◽  
Vol 717 ◽  
pp. 455-459
Author(s):  
Seung Gwan Lee ◽  
Seung Won Lee
Keyword(s):  
Simulation Experiment ◽  
State Transition ◽  
Optimization Problems ◽  
Ant Colony ◽  
Transition Rule ◽  
Ant Colony System ◽  
Combinatorial Optimization Problems ◽  
Maximum Time ◽  
Acs Algorithm ◽  
Better Than

Ant Colony System (ACS) is a new meta heuristics algorithms to solve hard combinatorial optimization problems. In this paper, we propose hybrid ant colony algotirhm that is searching the second best edge first in the state transition rule and updating the pheromone on edges applying the visited number of edge in the globally best tour. And we evaluate the proposed algorithm according to the maximum time for each trial. The results of a simulation experiment demonstrate that the proposed algorithm is better than, or, at least as good as, that of ACS algorithm in the most sets.

An Improved Ant Colony Optimization Supervised by PSO

2010 ◽  
Vol 108-111 ◽  
pp. 1354-1359
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Ant Colony Optimization ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Search Space ◽  
Ant Colony ◽  
Ant Colony System ◽  
Control Parameters ◽  
Evaporation Coefficient ◽  
The Core ◽  
Stochastic Selection

Combined with the idea of the particle swarm optimization (PSO) algorithm, the ant colony optimization (ACO) algorithm is presented to solve the well known traveling salesman problem (TSP). The core of this algorithm is using PSO to optimize the control parameters of ACO which consist of heuristic factor, pheromone evaporation coefficient and the threshold of stochastic selection, and applying ant colony system to routing. The new algorithm effectively overcomes the influence of control parameters of ACO and decreases the numbers of useless experiments, aiming to find the balance between exploiting the optimal solution and enlarging the search space.

The Optimal Research of Force Increasing Mechanism of Injection Machine

2011 ◽  
Vol 308-310 ◽  
pp. 899-907
Author(s):  
Tie Jun Li ◽  
Cheng Shi Zhu ◽  
Li Qun Yan ◽  
Li Xin ◽  
Jian Rong Ning
Keyword(s):  
Large Scale ◽  
Optimal Solution ◽  
Ant Colony ◽  
Design Parameters ◽  
Mathematics Model ◽  
Molding Machine ◽  
Total Length ◽  
Ant Colony Algorithms ◽  
Complete Optimization ◽  
Analysis Of Motion

The analysis of motion and mechanics property was carried out on the five hinged incline arranged and double elbowed force increasing mechanism of injection machine. A complete optimization designing procedure was carried out by improved ant colony algorithms, so as to increase the stroke ratio and the amplification of the force, and to decrease the total length of mechanism. Its optional mathematics model was established. The procedure of optimal design belongs to multi-object optimal problem. The optimal solution of the force increasing mechanism was found with improved ant colony algorithms. Compared with the traditional methods, the result shows that the total length of mechanism is decreased, the stroke ratio is increased, and the amplification of the force is increased. Furthermore, a sensitivity analysis of various design parameters has been performed to observe changes in injection performance parameters, and results show that the length of back elbowed bar and the length of connected bar have a significant impact on the performance measures. And the results recommend that the close clearance of the length of back elbowed bar and the length of connected bar must be maintained. Therefore it can provide some valuable instruction and theory to engineers. The instance of 1000 N large-scale injection molding machine is taken as an example to demonstrated that such method is effective and practical.

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