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Title: Hybrid evolutionary alogrithms and local search techniques
Author: Khanum, Rashida Adeeb
Awarding Body: University of Essex
Current Institution: University of Essex
Date of Award: 2013
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Population-based stochastic global search/optimisation algorithms often generate solutions with low accuracy. However, they cover the search space well; a property we refer to as exploration. In contrast, local optimisation algorithms, largely deterministic, find solutions with high accuracy when left to run long enough; they have the property we refer to as exploitation. Local optimisation algorithms, by their very nature, do not cover well the search space. It is also well known that some are more computationally demanding than others. It is, therefore, attractive to try and design algorithms which are good both at exploration and exploitation, but also have reasonable computing demands. A popular way to achieving this is to hybridise algorithms which have the desired properties. In this thesis, we consider the hybidisation of Adaptive Differential Evolution and Particle Swarm Optimisation algorithms with local search algorithms namely the Broyden-Fletcher-Goldfard-Shanno algorithm, the Steepest-Descent algorithm and the Nelder-Mead Simplex algorithm. Three combinations have been investigated. • Adaptive Differential Evolution with an Expensive Local Search Method, referred to as Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method; it combines a variant of Differential Evolution, Adaptive Differential Evolution with Optional External Archive, with the Broydon-Fletcher-Goldfarb-Shanno updating method, as the local search method. • Adaptive Differential Evolution and two local search algorithms, one expensive represented by Broydon-Fletcher-Goldfarb-Shanno, and the other comparatively cheap, represented by the Steepest Descent with a restart strategy; this is referred to as Hybridization of Adaptive Differential Evolution and Two Local Search Techniques with a Restart Strategy. • Particle Swarm Optimization algorithm with the Nelder-Mead Simplex algorithm, which is derivative-free, unlike the other two local search algorithms. This is referred to as A Hybridization of Particle Swarm Optimization with the Nelder-Mead Simplex Algorithm. These hybrid algorithms are then applied to unconstrained nonlinear optimization problems. Tests are carried out on well known problems from the Congress on Evolutionary Computation 2005(CEC2005) as well as those of the Congress on Evolutionary Computation 2010(CEC2010) test suits. Results show that improvements can be gained, but still at a cost. The thesis also contains an extensive review of the literature concerned with hybridization, particularly of evolutionary type algorithms with both classical and novel optimization approaches.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available