Comparative Study of Hybrid Genetic Algorithm for Tsp Biology Essay
The traveling salesman problem, TSP, is a classic combinatorial optimization problem. Many real problems can be solved by translating them into a TSP. The number of paths for a TSP increases exponentially with the number of cities, so it is an NP-complete problem and will maintain the standard for a new algorithm. As an evolution algorithm, a comparative analysis has been carried out between eight metaheuristic algorithms, namely genetic algorithm GA, differential evolution DE, particle swarm optimization PSO, gray wolf optimization GWO, improved gray wolf optimization IGWO, artificial bee colony ABC, locust optimization algorithm GOA, and a proposed . This article discusses the analysis of recent developments in genetic algorithms. The genetic algorithms that are of great interest in the research community are selected for analysis. This review will help the new and discerning researchers to provide a broader vision of genetic algorithms. The known algorithms and their implementation are: The current study focuses on the genetic algorithm and particle swarm optimization, and their combination into hybrid algorithms. Evolutionary algorithms are a subset of heuristic algorithms, with the Genetic Algorithm GA, introduced by Holland, 1 being one of the most widely used and well-known. This tutorial will implement the genetic algorithm optimization technique in Python based on a simple example where we try to maximize the output of an equation. The tutorial uses the decimal representation for genes, one-point crossover, and uniform mutation. 5 Note. This tutorial's GitHub project has been updated, with the current study focusing on the genetic algorithm and particle swarm optimization, and their combination into hybrid algorithms. Evolutionary algorithms are a subset of heuristic algorithms, where the genetic algorithm GA introduced by Holland, 1, is one of the most used and well-known: Construct an undirected multigraph GAB, V, EA ∪ EB by combining all edges of EA and E B .The edges belonging to EA or EB in GAB are labeled. Randomly divide all edges of GAB into AB cycles, where an AB cycle consists of alternately linked edges of EA and EB: Construct an E-set with a GUI that provides a genetic algorithm-based solution for solving the NP Traveling Salesman Problem. This graphical user interface GUI aims to solve the famous NP problem known as Traveling Salesman Problem, TSP, using a widely used artificial intelligence method: a genetic algorithm GA. Run 'main.m' for running. A hybrid form of GA with a particle swarm optimization algorithm, an iteration-based algorithm, is presented. Simulation results show that hybrid GA outperforms simple GA. Genetic algorithm GA has been proven to be efficient in optimization problems. It contains four operators including encryption, selection, crossover, particle swarm optimization and genetic algorithms. These are two classes of popular heuristic algorithms commonly used to solve complex multi-dimensional mathematical optimization problems, each with its own advantages and shortcomings. Particle swarm optimization is known to favor exploitation over exploration. This study examined the use of drones in the transportation sector and some optimization studies conducted to date in the literature. Based on these studies, a genetic algorithm-based method has been proposed to solve the multi-drone problem of hybrid trucks. With the proposed algorithm.