|dc.description.abstract||Optimization of systems plays an important role in various fields including mathematics, economics,
engineering and life sciences. A lot of real world optimization problems exist across field
of endeavours such as engineering design, space planning, networking, data analysis, logistic management,
financial planning, risk management, and a host of others. These problems are constantly
increasing in size and complexity, necessitating the need for improved techniques.
Many conventional approaches have failed to solve complex problems effectively due to increasingly
large solution space. This has led to the development of evolutionary algorithms that
draw inspiration from the process of natural evolution. It is believed that nature provides inspirations
that can lead to innovative models or techniques for solving complex optimization problems.
Among the class of paradigm based on this inspiration is Swarm Intelligence (SI).
SI is one of the recent developments of evolutionary computation. A SI paradigm is comprised
of algorithms inspired by the social behaviour of animals and insects. SI-based algorithms have
attracted interest, gained popularity and attention because of their flexibility and versatility. SIbased
algorithms have been found to be efficient in solving real world optimization problems.
Examples of SI algorithms include Ant Colony Optimization (ACO) inspired by the pheromone
trail-following behaviour of ant species; Particle Swarm Optimization (PSO) inspired by flocking
and swarming behaviour of insects and animals; and Bee Colony Optimization (BCO) inspired by
bees’ food foraging.
Recent emerging techniques in SI includes Roach-based Algorithms (RBA) motivated by cockroaches
social behaviour. Two recently introduced RBA algorithms are Roach Infestation Optimization
(RIO) and Cockroach Swarm Optimization (CSO) which have been applied to some
optimization problems to achieve competitive results when compared to PSO. This study is motivated
by the promising results of RBA, which have shown that the algorithms have potentials
to be efficient tools for solving optimization problems. Extensive studies of existing RBA were
carried out in this work revealing the shortcomings such as slow convergence and entrapment in
local minima. The aim of this study is to overcome the identified drawbacks. We investigate RBA
variants that are introduced in this work by introducing parameters such as constriction factor and
sigmoid function that have proved effective for similar evolutionary algorithms in the literature.
In addition components such as vigilance, cannibalism and hunger are incorporated into existing
RBAs. These components are constructed by the use of some known techniques such as simple
Euler, partial differential equation, crossover and mutation methods to speed up convergence and
enhance the stability, exploitation and exploration of RBA.
Specifically, a stochastic constriction factor was introduced to the existing CSO algorithm to
improve its performance and enhance its ability to solve optimization problems involving thousands
of variables. A CSO algorithm that was originally designed with three components namely
chase-swarming, dispersion and ruthlessness is extended in this work with hunger component to
improve its searching ability and diversity. Also, predator-prey evolution using crossover and mutation
techniques were introduced into the CSO algorithm to create an adaptive search in each
iteration thereby making the algorithm more efficient. In creating a discrete version of a CSO
algorithm that can be used to evaluate optimization problems with any discrete range value, we
introduced the sigmoid function.
Furthermore, a dynamic step-size adaptation with simple Euler method was introduced to the
existing RIO algorithm enhancing swarm stability and improving local and global searching abilities.
The existing RIO model was also re-designed with the inclusion of vigilance and cannibalism
The improved RBA were tested on established global optimization benchmark problems and
results obtained compared with those from the literature. The improved RBA introduced in this
work show better improvements over existing ones.||en