A comprehensive review of swarm optimization algorithms

Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained, and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches

A Comparison of GA Crossover and Mutation Methods for the Traveling Salesman Problem

The traveling salesman problem is a very popular combinatorial optimization problem in fields such as computer science, operations research, mathematics and optimization theory. Given a list of cities and the distances between any city to another, the objective of the problem is to find the optimal permutation (tour) in the sense of minimum traveled distance when visiting each city only once before returning to the starting city. Because many real-world problems can be modeled to fit this formulation, the traveling salesman problem has applications in challenges related to planning, routing, scheduling, manufacturing, logistics, and other domains. Moreover, the traveling salesman problem serves as a benchmark problem for optimization methods and algorithms, including the genetic algorithm. In this paper, we examine various implementations of the genetic algorithm for solving two examples of the traveling salesman problem. Specifically, we compare commonly employed methods of partially mapped crossover and order crossover with an alternative encoding scheme that allows for single-point, multipoint, and uniform crossovers. In addition, we examine several mutation methods, including Twors mutation, center inverse mutation, reverse sequence mutation, and partial shuffle mutation. We empirically compare the implementations in terms of the chosen crossover and mutation methods to solve two benchmark variations of the traveling salesperson problem. The experimental results show that the genetic algorithm with order crossover and the center inverse mutation method provides the best solution for the two test cases

Behaviour Study of an Evolutionary Design for Permutation Problems

International audienceThis paper studies an evolutionary representation/crossover combination for permutation problems, which are met in many application fields. Many efficient methods exist to solve these various variants. Increasing performances of computers also permitted to tackle more complex instances. But real-life applications make new conjunctions of constraints appear everyday. Then, searching new complementary ways to tackle efficiently these numerous constraints is still necessary. This paper focuses on such an approach. It deals with evolutionary algorithms, which have been already often used to solve permutation problems. It studies the behaviour of an evolutionary design, based on a Lehmer Code representation coupled with a simple n-point crossover. The goal is not to propose a new problem-tailored method which provides good performances for solving a given variant of problem or for a given class of benchmarks. The paper uses various measures to study the transmission of properties from parents to children, and the behaviour in terms of exploitation and exploration. The paper gives a review on related works, illustrates the issues which remain quite ill-understood for this representation and also gives experimental results by comparison with the permutation encoding more classically used in literature