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

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.

An on-line path planning of assembly robots based on FOVS

To improve the accuracy and efficiency of path planning for the mechanical assembly process of products, an on-line path planning method for mechanical assembly process robots based on visual field space is proposed in this paper. Firstly, to predict and describe the assembly process, the concept of field-of-view space (FOVS) is proposed. Secondly, image processing is carried out by knowledge base to judge the assembly type and current assembly state, and the initial assembly path is given. Then, the assembly process is integrated and solved, and the location estimation of obstacles are given according to the FOVS. Finally, the ant colony algorithm is improved to get the final assembly optimization path. Comparing the algorithm with the ACS algorithm in the aspect of path planning. The length of path planning is reduced by 2%, and the algorithm time is reduced by 0.5s, the accuracy and efficiency have been effectively improved. the result shows that the algorithm is effective.

Research on path planning of robot based on adaptive ACS fused with SHAA neural network

In this paper, we proposed an adaptive ACS algorithm by introducing an adaptive pheromone volatility coefficient and the algorithm diversity dynamically varying in different iterations of the algorithm. It incorporates a shunting hierarchical hybrid neural network application algorithm (Shunting HHNN Application Algorithm, SHAA) to overcome the drawbacks of global optimization capabilities of ant colony system (ACS) in solving robot path and easily being trapped into the local optimal solution. Considering the influence of the activation value size on the selection of the grid in the SHAA neural network algorithm, the distance factor and the activation value are combined to improve the heuristic function. This will not only ensure the convergence speed, but also avoid the premature stagnation and being trapped into a local optimal path. Simulation results show that the algorithm discussed in this paper outperforms better in both the global optimization ability and the robustness.

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails 𝜉 and 𝜌 using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters 𝜉 and 𝜌 on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter 𝜉 is more effective compared to the standard ACS.

A Novel Coplanar Waveguide-Fed Compact Microstrip Antenna for Future 5G Applications

In this study, a coplanar waveguide-fed compact microstrip antenna design for applications operating at higher 5G bands was proposed. The antenna with the compact size of 8 x 12.2 mm2 on FR4 substrate, having the dielectric constant of 4.3 and the height of 1.55 mm, was considered. The dimensions of the radiating patch and ground plane were optimized with the use of artificial cooperative search (ACS) algorithm to provide the desired return loss performance of the designed antenna. The performance analysis was done by using full-wave electromagnetic package programs based on the method of moment (MoM) and the finite integration technique (FIT). The 10 dB bandwidth for return loss results obtained with the use of the computation methods show that the proposed antenna performs well for 5G applications operating in the 24.25 – 27.50 GHz, 26.50 – 29.50 GHz, 27.50 – 28.35 GHz and 37 – 40 GHz frequency bands.

An improved ACS algorithm for data clustering

<span lang="EN-GB">Data clustering is a data mining technique that discovers hidden patterns by creating groups (clusters) of objects. Each object in every cluster exhibits sufficient similarity to its neighbourhood, whereas objects with insufficient similarity are found in other clusters. Data clustering techniques minimise intra-cluster similarity in each cluster and maximise inter-cluster dissimilarity amongst different clusters. Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. This algorithm minimises deterministic imperfections in which clustering is considered an optimisation problem. However, ACOC suffers from high diversification in which the algorithm cannot search for best solutions in the local neighbourhood. To improve the ACOC, this study proposes a modified ACOC, called M-ACOC, which has a modification rate parameter that controls the convergence of the algorithm. Comparison of the performance of several common clustering algorithms using real-world datasets shows that the accuracy results of the proposed algorithm surpasses other algorithms. </span>

The Effect of Updating the Local Pheromone on ACS Performance using Fuzzy Logic

Fuzzy Logic Controller (FLC) has become one of the most frequently utilised algorithms to adapt the metaheuristics parameters as an artificial intelligence technique. In this paper, the 𝜉 parameter of Ant Colony System (ACS) algorithm is adapted by the use of FLC, and its behaviour is studied during this adaptation. The proposed approach is compared with the standard ACS algorithm. Computational results are done based on a library of sample instances for the Traveling Salesman Problem (TSPLIB).