A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Copyright @ Springer-Verlag 2010.Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

Ant colony optimization with immigrants schemes in dynamic environments

This is the post-print version of this article. The official published version can be accessed from the link below - Copyright @ 2010 Springer-VerlagIn recent years, there has been a growing interest in addressing dynamic optimization problems (DOPs) using evolutionary algorithms (EAs). Several approaches have been developed for EAs to increase the diversity of the population and enhance the performance of the algorithm for DOPs. Among these approaches, immigrants schemes have been found beneficial for EAs for DOPs. In this paper, random, elitismbased, and hybrid immigrants schemes are applied to ant colony optimization (ACO) for the dynamic travelling salesman problem (DTSP). The experimental results show that random immigrants are beneficial for ACO in fast changing environments, whereas elitism-based immigrants are beneficial for ACO in slowly changing environments. The ACO algorithm with hybrid immigrants scheme combines the merits of the random and elitism-based immigrants schemes. Moreover, the results show that the proposed algorithms outperform compared approaches in almost all dynamic test cases and that immigrant schemes efficiently improve the performance of ACO algorithms in DTSP.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1

Evolutionary computation in dynamic and uncertain environments

This book can be accessed from the link below - Copyright @ 2007 Springer-Verla

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments

Copyright @ 2008 by the Massachusetts Institute of TechnologyIn recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/01

Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation tools for computationally expensive problems (CEPs). However, a randomly selected algorithm may fail in solving unknown problems due to no free lunch theorems, and it will cause more computational resource if we re-run the algorithm or try other algorithms to get a much solution, which is more serious in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce the risk of choosing an inappropriate algorithm for CEPs. We propose two portfolio frameworks for very expensive problems in which the maximal number of fitness evaluations is only 5 times of the problem's dimension. One framework named Par-IBSAEA runs all algorithm candidates in parallel and a more sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound (UCB) policy from reinforcement learning to help select the most appropriate algorithm at each iteration. An effective reward definition is proposed for the UCB policy. We consider three state-of-the-art individual-based SAEAs on different problems and compare them to the portfolios built from their instances on several benchmark problems given limited computation budgets. Our experimental studies demonstrate that our proposed portfolio frameworks significantly outperform any single algorithm on the set of benchmark problems

Open-Ended Evolutionary Robotics: an Information Theoretic Approach

This paper is concerned with designing self-driven fitness functions for Embedded Evolutionary Robotics. The proposed approach considers the entropy of the sensori-motor stream generated by the robot controller. This entropy is computed using unsupervised learning; its maximization, achieved by an on-board evolutionary algorithm, implements a "curiosity instinct", favouring controllers visiting many diverse sensori-motor states (sms). Further, the set of sms discovered by an individual can be transmitted to its offspring, making a cultural evolution mode possible. Cumulative entropy (computed from ancestors and current individual visits to the sms) defines another self-driven fitness; its optimization implements a "discovery instinct", as it favours controllers visiting new or rare sensori-motor states. Empirical results on the benchmark problems proposed by Lehman and Stanley (2008) comparatively demonstrate the merits of the approach