Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.699462
Title: Compact dynamic optimisation algorithm
Author: Uzor, Chigozirim
ISNI:       0000 0004 5989 6585
Awarding Body: De Montfort University
Current Institution: De Montfort University
Date of Award: 2015
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
In recent years, the field of evolutionary dynamic optimisation has seen significant increase in scientific developments and contributions. This is as a result of its relevance in solving academic and real-world problems. Several techniques such as hyper-mutation, hyper-learning, hyper-selection, change detection and many more have been developed specifically for solving dynamic optimisation problems. However, the complex structure of algorithms employing these techniques make them unsuitable for real-world, real-time dynamic optimisation problem using embedded systems with limited memory. The work presented in this thesis focuses on a compact approach as an alternative to population based optimisation algorithm, suitable for solving real-time dynamic optimisation problems. Specifically, a novel compact dynamic optimisation algorithm suitable for embedded systems with limited memory is presented. Three novel dynamic approaches that augment and enhance the evolving properties of the compact genetic algorithm in dynamic environments are introduced. These are 1.) change detection scheme that measures the degree of dynamic change 2.) mutation schemes whereby the mutation rates is directly linked to the detected degree of change and 3.) change trend scheme the monitors change pattern exhibited by the system. The novel compact dynamic optimization algorithm outlined was applied to two differing dynamic optimization problems. This work evaluates the algorithm in the context of tuning a controller for a physical target system in a dynamic environment and solving a dynamic optimization problem using an artificial dynamic environment generator. The novel compact dynamic optimisation algorithm was compared to some existing dynamic optimisation techniques. Through a series of experiments, it was shown that maintaining diversity at a population level is more efficient than diversity at an individual level. Among the five variants of the novel compact dynamic optimization algorithm, the third variant showed the best performance in terms of response to dynamic changes and solution quality. Furthermore, it was demonstrated that information transfer based on dynamic change patterns can effectively minimize the exploration/exploitation dilemma in a dynamic environment.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.699462  DOI: Not available
Keywords: Compact optimisation ; compact genetic algorithm ; adaptive mutation ; dynamic optimisation ; memory constrained dynamic optimisation
Share: