ABSTRACT

Dealing with optimization problems with more than one objective has been an important
research area in evolutionary computation. The class of multi-objective problems (MOPs)
is an important one because multi-objectivity exists in almost all aspects of human life;
whereby there usually exist several compromises in each problem. Multi-objective 
evolutionary algorithms (MOEAs) have been applied widely in many real-world problems.

This is because (1) they work with a population during the course of action, which hence
offer more flexible control to find a set of efficient solutions, and (2) real-world problems
are usually black-box where an explicit mathematical representation is unknown.
However, MOEAs usually require a large amount of computational effort. This is a substantial 
challenge in bringing MOEAs to practice. This thesis primarily aims to address
this challenge through an investigation into issues of scalability and the balance between
exploration and exploitation. These have been outstanding research challenges, not only
for MOEAs, but also for evolutionary algorithms in general. A distributed framework
of local models using explicit niching is introduced as an overarching umbrella to solve
multi-objective optimization problems. This framework is used to address the two-part
question about first, the role of communication messages and second, the role of explicit
niching in distributed evolutionary multi-objective optimization. The concept behind the
framework of local models is for the search to be conducted locally in different areas of the
decision search space, which allows the local models to be distributed on different 
processing nodes. During the optimization process, local models interact (exchange messages)
with each other using rules inspired from Particle Swarm Optimization (PSO). Hence, the
hypothesis of this work is that running simultaneously several search engines in different
local areas is better for exploiting local information, while exchanging messages among
those diverse engines can provide a better exploration strategy. For this framework, as
the models work locally, they gain access to some global knowledge of each other.

In order to validate the proposed framework, a series of experiments on a wide range of
test problems was conducted. These experiments were motivated by the following studies
which in their totality contribute to the verification of our hypothesis: (1) studying the
performance of the framework under different aspects such as initialization, convergence,
diversity, scalability, and sensitivity to the framework's parameters, (2) investigating 
interleaving guidance in both the decision and objective spaces, (3) applying local models
using estimation of distributions, (4) evaluating local models in noisy environments and
(5) the role of communication messages and explicit niching in distributed computing.
The experimental results showed that: (1) the use of local models increases the chance of
MOEAs to improve their performance in finding the Pareto optimal front, (2) interaction
strategies using PSO rules are suitable for controlling local models, and that they also
can be coupled with specialization in order to refine the obtained non-dominated set,
(3) estimation of distribution improves when coupled with local models, (4) local models
work well in noisy environments, and (5) the communication cost in distributed systems
with local models can be reduced significantly by using summary information (such as
the direction information naturally determined by local models) as the communication
messages, in comparison with conventional approaches using descriptive information of
individuals. In summary, the proposed framework is a successful step towards efficient
distributed MOEAs.