Comparative Analysis Of Genetic Algorithm Research Articles

A comparative analysis of genetic algorithms on a case study of asymmetric traveling salesman problem

A comparative analysis of genetic algorithms on a case study of asymmetric traveling salesman problem

Comparative Analysis of Genetic Algorithms Based on Different Selection Strategies for Refueling Optimization in the Ratio Method

The genetic algorithm is one of the classic algorithms applied to the refueling optimization. An important part of this algorithm is the selection strategies. The existing studies often directly adopt the roulette wheel selection and stochastic tournament selection, and are lacking in comparison and analysis of different selection strategies. To obtain the selection strategy with the strongest optimization capability, this study, with the 1/6 core of a thorium-based prismatic high-temperature gas-cooled reactor (HTGR) taken as an example, constructs the fitness function in the ratio method, performs core physics calculation using the DRAGON code, and in conjunction with the elitism strategy, compares the optimization capabilities of the five selection strategies, including the roulette wheel selection, stochastic tournament selection, uniform ranking method, exponential ranking selection and deterministic selection. The study results show that the optimization capability of the exponential ranking selection is superior to the other four strategies, so the exponential ranking selection is most suitable for solving the refueling optimization problems.

Comparative Analysis of Genetic Algorithm with Swarm Intelligence Algorithms

Traveling Salesman problem is the most recognized NP-hard problem for the researches in the field of computer science which focused on optimization. TSP finds the minimum travelling distance between given set of cities by traversing each of these cities only once except the starting city. This study discusses the salesman problem by presenting Particle Swarm Optimization Algorithm (PSO), Ant Colony Optimization algorithm (ACO) and Genetic Algorithm (GA) which intends to solve the problem. In this paper, these algorithms are applied on the benchmark TSP dataset Oliver 30. Also, the paper provides comparison between these algorithms and this comparison helps to choose the better algorithm.

Comparative Analysis of Genetic Algorithms and Particle Swarm Optimization Algorithms for Optimal Reservoir Operation

Apart from traditional optimization techniques, modern heuristic optimization techniques, like genetic algorithms (GA), particle swarm optimization algorithm (PSO) have been widely used to solve optimization problems. This paper deals with comparative analysis of GA and PSO and their applications in a reservoir operation problem. Extensive component analysis, parameter sensitivity analysis of GA and PSO show that both GA and PSO can be used for optimal reservoir operation, but they display different features. GA can obtain very high approximate global optimal solutions of the problem with a high stability and a high computing efficiency, but it can’t obtain the problem’s accurate global optimal solutions. For GA, population size and mutation rate are two main parameters affect its solution qualities. Comparative to GA, PSO can obtain the accurate global optimal solutions of the problem with a higher computing efficiency, but with a less stability. For PSO, population size and velocity parameter are two main parameters affect its solution qualities.

A comparative analysis of genetic algorithms and directed grid search for parametric optimization

In this paper, a comparative analysis of the performance of the Genetic Algorithm (GA) and Directed Grid Search (DGS) methods for optimal parametric design is presented. A genetic algorithm is a guided random search mechanism based on the principle of natural selection and population genetics. The Directed Grid Search method uses a selective directed search of grid points in the direction of descent to find the minimum of a real function, when the initial estimate of the location of the minimum and the bounds of the design variables are specified. An experimental comparison and a discussion on the performance of these two methods in solving a set of eight test functions is presented.