9 research outputs found
Quick Combinatorial Artificial Bee Colony -qCABC- Optimization Algorithm for TSP
Combinatorial Artificial Bee Colony Algorithm (CABC) is a new version of Artificial Bee Colony (ABC) to solve combinatorial type optimization problems and quick Artificial Bee Colony (qABC) algorithm is an improved version of ABC in which the onlooker bees behavior is modeled in more detailed way. Studies showed that qABC algorithm improves the convergence performance of standard ABC on numerical optimization. In this paper, to see the performance of this new modeling way of onlookers' behavior on combinatorial optimization, we apply the qABC idea to CABC and name this new algorithm as quick CABC (qCABC). qCABC is tested on Traveling Salesman Problem and simulation results show that qCABC algorithm improves the convergence and final performance of CABC
Evolving Diverse Sets of Tours for the Travelling Salesperson Problem
Evolving diverse sets of high quality solutions has gained increasing
interest in the evolutionary computation literature in recent years. With this
paper, we contribute to this area of research by examining evolutionary
diversity optimisation approaches for the classical Traveling Salesperson
Problem (TSP). We study the impact of using different diversity measures for a
given set of tours and the ability of evolutionary algorithms to obtain a
diverse set of high quality solutions when adopting these measures. Our studies
show that a large variety of diverse high quality tours can be achieved by
using our approaches. Furthermore, we compare our approaches in terms of
theoretical properties and the final set of tours obtained by the evolutionary
diversity optimisation algorithm.Comment: 11 pages, 3 tables, 3 figures, to be published in GECCO '2