An immune system based genetic algorithm using permutation-based dualism for dynamic traveling salesman problems

Copyright @ Springer-Verlag Berlin Heidelberg 2009.In recent years, optimization in dynamic environments has attracted a growing interest from the genetic algorithm community due to the importance and practicability in real world applications. This paper proposes a new genetic algorithm, based on the inspiration from biological immune systems, to address dynamic traveling salesman problems. Within the proposed algorithm, a permutation-based dualism is introduced in the course of clone process to promote the population diversity. In addition, a memory-based vaccination scheme is presented to further improve its tracking ability in dynamic environments. The experimental results show that the proposed diversification and memory enhancement methods can greatly improve the adaptability of genetic algorithms for dynamic traveling salesman problems.This work was supported by the Key Program of National Natural Science Foundation (NNSF) of China under Grant No. 70431003 and Grant No. 70671020, the Science Fund for Creative Research Group of NNSF of China under GrantNo. 60521003, the National Science and Technology Support Plan of China under Grant No. 2006BAH02A09 and the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1

Self-adaptation of mutation operator and probability for permutation representations in genetic algorithms

The choice of mutation rate is a vital factor in the success of any genetic algorithm (GA), and for permutation representations this is compounded by the availability of several alternative mutation operators. It is now well understood that there is no one "optimal choice"; rather, the situation changes per problem instance and during evolution. This paper examines whether this choice can be left to the processes of evolution via selfadaptation, thus removing this nontrivial task fromtheGAuser and reducing the risk of poor performance arising from (inadvertent) inappropriate decisions. Self-adaptation has been proven successful for mutation step sizes in the continuous domain, and for the probability of applying bitwise mutation to binary encodings; here we examine whether this can translate to the choice and parameterisation of mutation operators for permutation encodings. We examine one method for adapting the choice of operator during runtime, and several different methods for adapting the rate at which the chosen operator is applied. In order to evaluate these algorithms, we have used a range of benchmark TSP problems. Of course this paper is not intended to present a state of the art in TSP solvers; rather, we use this well known problem as typical of many that require a permutation encoding, where our results indicate that self-adaptation can prove beneficial. The results show that GAs using appropriate methods to self-adapt their mutation operator and mutation rate find solutions of comparable or lower cost than algorithms with "static" operators, even when the latter have been extensively pretuned. Although the adaptive GAs tend to need longer to run, we show that is a price well worth paying as the time spent finding the optimal mutation operator and rate for the nonadaptive versions can be considerable. Finally, we evaluate the sensitivity of the self-adaptive methods to changes in the implementation, and to the choice of other genetic operators and population models. The results show that the methods presented are robust, in the sense that the performance benefits can be obtained in a wide range of host algorithms. © 2010 by the Massachusetts Institute of Technology

A Hybrid Genetic Algorithm for the min-max Multiple Traveling Salesman Problem

This paper proposes a hybrid genetic algorithm for solving the Multiple Traveling Salesman Problem (mTSP) to minimize the length of the longest tour. The genetic algorithm utilizes a TSP sequence as the representation of each individual, and a dynamic programming algorithm is employed to evaluate the individual and find the optimal mTSP solution for the given sequence of cities. A novel crossover operator is designed to combine similar tours from two parents and offers great diversity for the population. For some of the generated offspring, we detect and remove intersections between tours to obtain a solution with no intersections. This is particularly useful for the min-max mTSP. The generated offspring are also improved by a self-adaptive random local search and a thorough neighborhood search. Our algorithm outperforms all existing algorithms on average, with similar cutoff time thresholds, when tested against multiple benchmark sets found in the literature. Additionally, we improve the best-known solutions for 21 out of 89 instances on four benchmark sets

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Development of an automated aircraft subsystem architecture generation and analysis tool

Purpose – The purpose of this paper is to present a new computational framework to address future preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate technologies forming subsystem architectures is enabled with the provision of automated architecture generation, analysis and optimization. Main focus lies with a demonstration of the frameworks workings, as well as the optimizers performance with a typical form of application problem. Design/methodology/approach – The core aspects involve a functional decomposition, coupled with a synergistic mission performance analysis on the aircraft, architecture and component levels. This may be followed by a complete enumeration of architectures, combined with a user defined technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both component composition and design parameters. The optimizer is tested on a generic architecture design problem combined with modified Griewank and parabolic functions for the continuous space. Findings – Insights from the generalized application problem show consistent rediscovery of the optimal architectures with the optimizer, as compared to a full problem enumeration. In addition multi-objective optimization reveals a Pareto front with differences in component composition as well as continuous parameters. Research limitations/implications – This paper demonstrates the frameworks application on a generalized test problem only. Further publication will consider real engineering design problems. Originality/value – The paper addresses the need for future conceptual design methods of complex systems to consider a mixed concept space of both discrete and continuous nature via automated methods

Hybridization of Biologically Inspired Algorithms for Discrete Optimisation Problems

In the field of Optimization Algorithms, despite the popularity of hybrid designs, not enough consideration has been given to hybridization strategies. This paper aims to raise awareness of the benefits that such a study can bring. It does this by conducting a systematic review of popular algorithms used for optimization, within the context of Combinatorial Optimization Problems. Then, a comparative analysis is performed between Hybrid and Base versions of the algorithms to demonstrate an increase in optimization performance when hybridization is employed