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Proceedings ArticleDOI

Comparing Performance of Evolutionary Algorithms - A Travelling Salesman Perspective

TL;DR: In this article, a comparison of four optimization algorithms in solving the Travelling Salesman Problem is presented, i.e., pure genetic algorithm, particle swarm optimization, ant colony optimization, and simulated Annealing algorithm.
Abstract: The recent advancements in the fields of computer science and engineering have led to strides in several multi-disciplinary and trans-disciplinary scientific research areas. The Travelling Salesman Problem is one such classical optimization problem that is widely used in various fields including Social Net-work analysis, traffic flow control, human resource management, logistic planning, etc. Attempts have been made in the past to solve this problem with different computational techniques including basic dynamic programming, neural networks to several others. This paper presents a comparison of four optimization algorithms in solving the Travelling Salesman Problem. The chosen approaches are the classical pure Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO) and the Simulated Annealing algorithm (SA). The paper compares and contrasts the performance based on the problem-solving exercises.
Citations
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Journal ArticleDOI
TL;DR: Developing models to estimate the expected length of the shortest path of 3D-TSP, an extension of TSP in three-dimensional space, which plays an important role in the fields of3D path planning and UAV inspection, and provides important references for many applications.
Abstract: Finding the shortest path of the traveling salesman problem (TSP) is a typical NP-hard problem and one of the basic optimization problems. TSP in three-dimensional space (3D-TSP) is an extension of TSP. It plays an important role in the fields of 3D path planning and UAV inspection, such as forest fire patrol path planning. Many existing studies have focused on the expected length of the shortest path of TSP in 2D space. The expected length of the shortest path in 3D space has not yet been studied. To fill this gap, this research focuses on developing models to estimate the expected length of the shortest path of 3D-TSP. First, different experimental scenarios are designed by combining different service areas and the number of demand points. Under each scenario, the specified number of demand points is randomly generated, and an improved genetic algorithm and Gurobi are used to find the shortest path. A total of 500 experiments are performed for each scenario, and the average length of the shortest path is calculated. The models to estimate the expected length of the shortest path are proposed. Model parameters are estimated and k-fold cross-validation is used to evaluate the goodness of fit. Results show that all the models fit the data well and the best model is selected. The developed models can be used to estimate the expected length of the shortest path of 3D-TSP and provide important references for many applications.

2 citations

Proceedings ArticleDOI
01 Jul 2021
TL;DR: In this article, the identification and detection of forgery in the monetary notes is performed by using deep neural-based genetic algorithms, where an Artificial Neural Network was used for training on the banknote dataset and the learned weights were vectorized for genetic algorithm processing.
Abstract: Metaheuristic algorithms aim to find high performing, near-optimal solutions with reasonable computing costs. Using the process of natural selection that belongs to a set of evolutionary algorithms, are Genetic Algorithms. These algorithms intelligently exploit random search over the data in search of a solution space for better performance. Detection of forgery in legal documents by an automated system remains a valid problem of today. In this paper, the identification and detection of forgery in the monetary notes is performed by using deep neural-based Genetic Algorithms. An Artificial Neural Network was used for training on the banknote dataset and the learned weights were vectorized for Genetic Algorithm processing. Genetic Algorithm is explained in detail along with the working of the Fitness model that determines the efficiency of the trained model. The model achieved a high-performance accuracy of 94% with the fitness score of95.2. The improvement of the model with the infusion of trainable weights from a deep neural model is represented and analyzed.
Book ChapterDOI
01 Jan 2023
TL;DR: In this paper , a regression model is used to unravel the trip time cost between two points, and the algorithm optimizes the travelling salesman path using four biologically inspired meta-heuristics: genetic evolution, ant colony, grey wolf, and artificial bee colony.
Abstract: Planning the most energy-effective and fastest route is pivotal to reducing the windshield time of field workers, ensuring guests receive their packages at the listed time and dwindling the logistic cost. One of the numerous benefits of route optimization is that all the parcels are delivered with the most systematized use of resources. Route optimization enables the computation of the fastest and most energy-effective route while taking into account multiple stops and limited delivery time windows. It solves the vehicle route problem and travelling salesman problem. Using a collection of New York City locales represented as longitude and latitude coordinates, the algorithm searches the solution space to find the most effective travelling salesman path which takes the least amount of time to travel. By creating a regression model to unravel the trip time cost between two points, the algorithm optimizes the travelling salesman path using four biologically inspired meta-heuristics: Genetic Evolution, ant colony, grey wolf, and Artificial Bee Colony.
References
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Journal ArticleDOI
13 May 1983-Science
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Abstract: There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

41,772 citations

Journal ArticleDOI
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed The relationships between particle swarm optimization and both artificial life and genetic algorithms are described

18,439 citations

Book
05 May 1993

2,454 citations

Journal ArticleDOI
TL;DR: An elaborate comparative analysis is carried out to endow these algorithms with fitness sharing, aiming to investigate whether this improves performance which can be implemented in the evolutionary algorithms.
Abstract: For a decade swarm Intelligence, an artificial intelligence discipline, is concerned with the design of intelligent multi-agent systems by taking inspiration from the collective behaviors of social insects and other animal societies. They are characterized by a decentralized way of working that mimics the behavior of the swarm. Swarm Intelligence is a successful paradigm for the algorithm with complex problems. This paper focuses on the comparative analysis of most successful methods of optimization techniques inspired by Swarm Intelligence (SI) : Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). An elaborate comparative analysis is carried out to endow these algorithms with fitness sharing, aiming to investigate whether this improves performance which can be implemented in the evolutionary algorithms.

229 citations

Journal ArticleDOI
TL;DR: Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.
Abstract: Focused on a variation of the euclidean traveling salesman problem (TSP), namely, the generalized traveling salesman problem (GTSP), this paper extends the ant colony optimization method from TSP to this field. By considering the group influence, an improved method is further improved. To avoid locking into local minima, a mutation process and a local searching technique are also introduced into this method. Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.

210 citations