This paper aims to provide a comparative study of the different artificial intelligence (AI) algorithms applied to solve the traveling salesman problem (TSP) Four (4) AI algorithms such as genetic algorithm, nearest neighbor, ant colony optimization, and neuro-fuzzy are executed in MatLab software to determine which among these techniques will provide the least execution time to solve a TSP The objective of comparing and analyzing each AI algorithm — as applied to a single problem with the different program execution — is to identify if significant difference in execution time could lead to significant saving in energy consumption The simulations using MatLab resulted to strong correlation at an R2 of 095 in the average execution time with the number of code lines, but do not give a significant execution time variance as when ANOVA and t-test measures were performed The result of this paper could be used as a basis in the design phase of software development life cycle to arrive into an energy efficient software application with respect to time needed to execute a program

Comparative analysis of solving traveling salesman problem using artificial intelligence algorithms

Terahertz spectroscopy and imaging are a rapidly developed technique with important applications in many areas, such as medical imaging, security, chemistry, biochemistry, astronomy, communications, and manufacturing, to name a few. However, terahertz spectroscopy and imaging produce excessively high dimensional data which is prohibitive for common methods developed in the area of image processing. In this paper, we report our recent study on a novel classifier based on feature extraction using approximate entropy (ApEn). The classifier is initiated by analyzing the complexity of the terahertz spectrum, which is then combined with a deep neural network for material classification. Experimental results show that approximate entropy based features have high sensitive for detecting metal matrix composites, the accuracy of identification is up to 96.3%. Related algorithms for ApEn feature extraction and material classification are illustrated. An optimal parameter-embedding dimension, subject to classification accuracy for ApEn is studied.

Terahertz spectroscopic material identification using approximate entropy and deep neural network

This paper proposes a general algorithm framework for solving dynamic sequence optimization problems (DSOPs). The framework adapts a novel genetic learning (GL) algorithm to dynamic environments via a clustering-based multi-population strategy with a memory scheme, namely, multi-population GL (MPGL). The framework is instantiated for a 3D dynamic shortest path problem, which is developed in this paper. Experimental comparison studies show that MPGL is able to quickly adapt to new environments and it outperforms several ant colony optimization variants.

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Memory-based multi-population genetic learning for dynamic shortest path problems

Ant colony optimization (ACO) has been shown effectiveness for solving combinatorial optimization problems. Inspired by the basic principles of ACO, this paper proposes a novel evolutionary algorithm, called probabilistic learning (PL). In the algorithm, a probability matrix is created based on weighted information of the population and a novel random search operator is proposed to adapt the PL to dynamic environments. The algorithm is tested on the shortest path problem (SPP) in both static and dynamic environments. Experimental results on a set of carefully designed 3D-problems show that the PL algorithm is effective for solve SPPs and outperforms several popular ACO variants.

A Probabilistic Learning Algorithm for the Shortest Path Problem

Shape formation for a group of agents is crucial in many strategy games as it brings collective payoff through cooperations. Finding a good formation for a group of agents in games is considered as a combinatorial optimization problem in this paper. To address this issue, this paper proposes a novel and simple evolutionary algorithm based on a probability learning matrix, which is the foundation of a formation learning and discovery mechanism. Experimental results on several test problems show that the proposed algorithm works well in comparison with another peer algorithm.

Shape Formation in Games: A Probability-Based Evolutionary Approach

The travelling salesman problem is a well known combinatorial optimization problem and evolutionary computation methods are one of the important methods to solve it. A difficult issue for evolutionary computation methods is to identify good edges that belong to the global optimum during the search progress. To address this issue, this paper proposes a tour construction algorithm, which is based on a memory-based statistical learning mechanism. A probability matrix is created according to the edge distribution in a memory population, which stores the best solution found by every individual. For each individual, a tour is constructed according to its local personal best solution found so far and the global probability matrix. Two variants of ant colony optimization are chosen to test the effectiveness of the proposed algorithm. The result show that the proposed algorithm is an efficient learning algorithm for the travelling salesman problem.

Memory-based statistical learning for the travelling salesman problem

To solve a travelling salesman problem by evolutionary algorithms, a challenging issue is how to identify promising edges that are in the global optimum. The paper aims to provide solutions to improve existing algorithms and to help researchers to develop new algorithms, by considering such a challenging issue. In this paper, three heuristic strategies are proposed for population based algorithms. The three strategies, which are based on statistical information of population, the knowledge of minimum spanning tree, and the distance between nodes, respectively, are used to guide the search of a population. The three strategies are applied to three existing algorithms and tested on a set of problems. The results show that the algorithms with heuristic search perform better than the originals.

Yong Xia

Papers

Memory-based statistical learning for the travelling salesman problem

Three New Heuristic Strategies for Solving Travelling Salesman Problem