Contrasting Evolutionary Algorithm and Social Spider Algorithm using Travelling Salesman Problem

Eman Arif; Asma Sanam Larik; Tasneem Adnan; Maryam Raees Ahmed

Eman Arif
Asma Sanam Larik
Tasneem Adnan
Maryam Raees Ahmed

Keywords: Social Spider, Optimization, Evolutionary Algorithm, Swarm Intelligence

Abstract

Combinatorial optimization problems have plenty of attention, both for the reasons of their complexity and for their practical applications. Looking at the range of metaheuristic techniques, there are two which have shown good results: the Genetic Algorithms (GA) and swarm-based techniques. A comparative study of the Genetic Algorithm and the Social Spider Algorithm (SSA) is described in this paper with Travelling Salesman Problem (TSP) as benchmark. The SSA, which is based on cooperative interaction mechanisms among the agents, is modified to address the TSP and its performance is compared to GA. The algorithms are contrasted with respect to the quality of solutions and efficiency of computation. The experimental results demonstrate that the convergence property and runtime efficiency of Social Spiders improves the problem considered. The paper is arranged as follows: In Section 1 and 2, is about introduction to the paperwork discussion of literature gaps section 3 we introduce the concept of Genetic algorithm and Swarm While Section 4, the problem is formulated. Section 5 focuses on implementation of Social Spider Algorithm and 6 presents experimental results and comparison of the two algorithms at hand: Genetic, Social Spider Optimization. Finally, in Section 6 conclusions are drawn up and prospects are discussed.

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