This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

• A cooperative co-evolutionary DE with dynamic hybrid mechanism (QGDECC) is proposed. • A quantum variable decomposition strategy is designed by utilizing qubit string. • The increment mutation method is proposed by using the searched evolution information. • A railway train delay scheduling method based on QGDECC is proposed. In order to effectively schedule railway train delay, an adaptive cooperative co-evolutionary differential evolution with dynamic hybrid mechanism of the quantum evolutionary algorithm and genetic algorithm, named QGDECC is designed in this paper. In the QGDECC, the quantum variable decomposition strategy is designed by utilizing qubit string to decompose variables adaptively according to the coevolution performance. Then the increment mutation method is proposed to improve the convergence speed which make full use of searched evolution information. Besides, the parameter adaptive strategy is deeply explored for strengthening the robust of the algorithm. The QGDECC with global search capability is employed to realize a railway train delay scheduling method for effectively eliminating the impact of train delay. Finally, several benchmark functions and actual train operation data are selected to verify the optimization performance of QGDECC. The experimental results show that QGDECC has higher adaptability, faster convergence speed and accuracy. The train delay scheduling method can effectively eliminate the impact of delay on the railway network, and minimize the gap between the rescheduled train schedule and the original train schedule. 

Dynamic hybrid mechanism-based differential evolution algorithm and its application

Novel discrete approach for combinational optimization based on cuckoo optimization algorithm (COA).Redefining the difference concept between two habitats as a differential list of movements.Proposed method enable to solve non-permutation problems.Modifying egg laying and immigration phase of COA in the proposed discrete cuckoo optimization algorithm (DCOA).High quality results obtained for graph coloring problems. In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature, such as particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo optimization algorithm (COA). Recently introduced COA, has proven its excellent capabilities, such as faster convergence and better global minimum achievement. In this paper a new approach for solving graph coloring problem based on COA was presented. Since COA at first was presented for solving continuous optimization problems, in this paper we use the COA for the graph coloring problem, we need a discrete COA. Hence, to apply COA to discrete search space, the standard arithmetic operators such as addition, subtraction and multiplication existent in COA migration operator based on the distance's theory needs to be redefined in the discrete space. Redefinition of the concept of the difference between the two habitats as the list of differential movements, COA is equipped with a means of solving the discrete nature of the non-permutation. A set of graph coloring benchmark problems are solved and its performance is compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method.

Modified cuckoo optimization algorithm (MCOA) to solve graph coloring problem

The use of rigid robot manipulators with good performance in industrial applications demands a proper robust and optimized control technique. Several works have proven the efficient use of metaheuristics optimization algorithms to work with complex problems in the robotic area. In this work, it is proposed the use of Grey Wolf Optimizer (GWO) with chaotic basis to optimize the parameters of a robust Higher Order Sliding Modes (HOSM) controller for the position control in joint space of a rigid robot manipulator. A total of seven test cases were considered varying the chosen chaotic map, face to the original GWO and the general repeatability of such algorithm is improved using chaotic versions. Also, two cost functions were tested within the HOSM optimization. Simulation results suggest that both algorithm and cost function formulations influence the chaotic map choice. In fact, the chattering problem, presented by HOSM controllers, is reduced when the cost function attempts to minimize the total variation of the control signal.

Chaos-based grey wolf optimizer for higher order sliding mode position control of a robotic manipulator

Graph coloring problem (GCP) is getting more popular to solve the problem of coloring the adjacent regions in a map with minimum different number of colors. It is used to solve a variety of real-world problems like map coloring, timetabling and scheduling. Graph coloring is associated with two types of coloring as vertex and edge coloring. The goal of the both types of coloring is to color the whole graph without conflicts. Therefore, adjacent vertices or adjacent edges must be colored with different colors.  The number of the least possible colors to be used for GCP is called chromatic number. As the number of vertices or edges in a graph increases, the complexity of the problem also increases. Because of this, each algorithm can not find the chromatic number of the problems and may also be different in their executing times. Due to these constructions, GCP is known an NP-hard problem. Various heuristic and metaheuristic methods have been developed in order to solve the GCP. In this study, we described First Fit (FF), Largest Degree Ordering (LDO), Welsh and Powell (WP), Incidence Degree Ordering (IDO), Degree of Saturation (DSATUR) and Recursive Largest First (RLF) algorithms which have been proposed in the literature for the vertex coloring problem and these algorithms were tested on benchmark graphs provided by DIMACS. The performances of the algorithms were compared as their solution qualities and executing times. Experimental results show that while RLF and DSATUR algorithms are sufficient for the GCP, FF algorithm is generally deficient. WP algorithm finds out the best solution in the shortest time on Register Allocation, CAR, Mycielski, Stanford Miles, Book and Game graphs. On the other hand, RLF algorithm is quite better than the other algorithms on Leighton, Flat, Random (DSJC) and Stanford Queen graphs.

https://www.ijisae.org/IJISAE/article/download/911/491

A Performance Comparison of Graph Coloring Algorithms

We present a hybrid chaotic ant swarm approach for the graph coloring problem (CASCOL). This approach is based on a novel swarm intelligence technique called chaotic ant swarm (CAS) and a simple greedy sequential coloring, First-Fit algorithm. We use the CAS evolutionary to improve First-Fit algorithm for GCP and formulate hybrid algorithm architecture. Computational experiments give evidence that our proposed algorithm is competitive with the existing algorithms for this problem.

Chaotic ant swarm for graph coloring

In this paper, we propose a decentralized coordination algorithm for a group of mobile nodes, called an autonomous swarm, on a finite two-dimensional space, which can efficiently coordinate cooperatively the autonomous swarm to the optimal solution. Our algorithm is inspired by chaotic behavior of a single ant and self-organization behavior of the whole ant colony. To construct this algorithm, we firstly assume that each agent is a nonlinear oscillator presenting the chaotic behavior of a single ant. Then we establish a self-organization mechanism according to the self-organization behavior of the whole ant colony. Moreover, we analyze the convergence of the proposed algorithm. Finally, we experimentally evaluate the performance of our algorithm with the clustering and dispersion operations of a swarm. Comparison results of the proposed algorithm and the gradient-type one are also presented to illustrate the effectiveness of the proposed scheme in approximately global optimization for swarms.

Decentralized coordination of autonomous swarms inspired by chaotic behavior of ants

Chaotic ant swarm (CAS) is an optimization algorithm based on swarm intelligence theory, which has been applied to find the global optimum solution in continuous search space. However, it is not able to solve the combinational optimization problem directly, e.g., the traveling salesman problem (TSP). To tackle this problem, we propose a new method to solve the traveling salesman problem based on chaotic ant swarm, CAS-TSP for short. The CAS-TSP is developed by introducing a mapping from continuous space to discrete space, reverse operator and crossover operator into the CAS. Computer simulations demonstrate that the CAS-TSP is capable of generating optimal solution to instances of the TSPLIB in almost all test problems of sizes up to 150. Also a comparative computational study shows that this CAS-TSP algorithm is an efficient tool for solving TSP and this heuristic is competitive also with other heuristics.

Chaotic ant swarm for the traveling salesman problem

Large-scale multiobjective optimization with adaptive competitive swarm optimizer and inverse modeling

With the in-depth reform of intelligent manufacturing, selecting high-quality intelligent manufacturing system solution suppliers has become a key force to promote the intelligent transformation of manufacturing enterprises. However, manufacturing enterprises have hidden risks in the selection process of many intelligent manufacturing system solution suppliers, so it is urgent to carry out the research on the risk evaluation of intelligent manufacturing system solution suppliers. Based on the current situation in China’s intelligent manufacturing industry, this paper constructs the evaluation index system of intelligent manufacturing system solution suppliers, uses the PLS-SEM method to establish the risk evaluation model of intelligent manufacturing system solution suppliers, collects data through a questionnaire survey, uses a PLS algorithm to fit the index and test the model, and uses power BI software to visualize the risky impact. The conclusions are as follows: (1) The primary indicators have hidden risks for the system solution suppliers. (2) The higher the achievement of secondary indicators, the lower the implied risk, and the more conducive to the intelligent upgrading of manufacturing enterprises. According to the visualization results, management suggestions are given to provide useful reference for manufacturing enterprises to select high-quality intelligent manufacturing system solution suppliers and promote the transformation and upgrading of manufacturing enterprises, from digitization and networking to intelligent stage.

/pdf/risk-assessment-in-supplier-selection-for-intelligent-3c6qqwpj.pdf

Risk Assessment in Supplier Selection for Intelligent Manufacturing Systems Based on PLS-SEM

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