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Abstract
Abstract
In recent years, the exponential growth in computing power and the existence of
massive datasets integrated with the algorithms’ improvement led to an unparalleled
surge of interest in machine learning topic. Nowadays, machine learning algorithms are
successfully applied in a wide range of domains. It has been employed for regression,
classification, dimensional reduction, especially for high-dimensional datasets and clustering.
In fact, machine learning algorithms played a remarkable role in huge parts of
our daily life such as anomaly detection, medical diagnosis, email/spam filtering, web
searches, credit card fraud detection, financial analysis, and many more. Although machine
learning proved its efficiency in many fields, it has several problems, which can
be categorized as follows: feature selection, classification, regression, rule extraction,
clustering, contrast enhancement of images and parameters setting.
Recently, nature-inspired meta-heuristic algorithms have become influential and
powerful in many applications. They have been used as efficient tools to deal with machine
learning problems. Swarm intelligence algorithms are a class of meta-heuristic
optimization algorithms. However, swarm intelligent algorithms have been shown to
optimize a wide range of problems successfully; some of these algorithms often suffer
from premature convergence and entrapment in a local optimum, especially in complex
optimization problems. Therefore, several studies have been proposed in literature to
boost the performance of these algorithms and to overcome these problems. The main
goals of the thesis are to introduce enhanced versions of swarm intelligence algorithms
and to apply the proposed algorithms on different optimization problems. In the present
study, we are mainly focused on boosting the performance of the Salp Swarm Algorithm
(SSA), and Grasshopper Optimization Algorithm (GOA), where three different enhancing
methods are used. These methods are the principles of chaos theory, the random walk
of l´evy-flight method and the principles of quantum computing. Also, four optimization
problems are considered. These problems are global optimization problem, feature selection
optimization problem, contrast enhancement of images optimization problem and
finally, parameters setting optimization problem. Each one of the proposed algorithms
is applied to solve two optimization problems. Meanwhile, all the proposed algorithms
are applied to the global optimization problem.
SSA is one of the recently proposed algorithms driven by the simulation behavior of
salps. However, like most of the meta-heuristic algorithms, it suffered from stagnation
in local optima and low convergence rate. Recently, chaos theory has been successfully
applied to solve these problems. In this thesis, a novel hybrid approach based on SSA
and the chaos theory is proposed. The proposed Chaotic Salp Swarm Algorithm (CSSA)
is applied to global optimization and feature selection optimization problems, where
fourteen unimodal and multimodal global benchmark optimization problems and twenty
benchmark datasets are adopted. Additionally, ten different chaotic maps are employed
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to enhance the convergence rate and resulting precision. Simulation results showed that
the proposed CSSA is a promising algorithm. Also, the results showed the capability of
CSSA in finding an optimal feature subset, which maximizes the classification accuracy,
while minimizing the number of selected features. Moreover, the results showed that
logistic chaotic map is the optimal map of the used ten, which can significantly boost
the performance of original SSA.
Additionally, a modified GOA based on the random walk of L´evy-flight method,
called as (LevyGOA) is proposed. GOA is one of the recently meta-heuristic optimization
algorithms. Although, GOA has shown good performance, it still has demerits
respect to low precision, slow convergence and easily stuck at local minima. The experimental
results showed that LevyGOA able to provide a better trade-off between exploitation
and exploration, which makes LevyGOA faster and more robust than GOA.
LevyGOA is further compared with other meta-heuristic optimization algorithms and
the basic GOA for solving two optimization problems. These problems are global optimization
problem and parameters optimization of SVM, where two global benchmark
functions and six well-known benchmark datasets are used. The experimental results
showed that LevyGOA could significantly improve the performance of GOA. The results
demonstrated that LevyGOA outperforms the other algorithms on a majority of
the benchmark functions and benchmark datasets.
Finally, a new hybrid approach based on quantum computing and SSA, called
Quantum Salp Swarm Algorithm (QSSA) is proposed. The proposed QSSA is used to
boost the performance of SSA through finding the optimal trade-off between exploitation
and exploration. QSSA relies on embedding the integration of the quantum operators
and interference in the optimization process of SSA and the quantum representation
of the search space. The proposed QSSA is applied to global optimization and contrast
enhancement of images optimization problems. It is tested on twenty-nine global
benchmark functions and eight benchmark images. The simulation results showed that
employing the principles of quantum can significantly boost the performance of SSA.
Also, the performance of QSSA is compared with SSA and other recent and well-known
optimization algorithms. The results on global benchmark datasets demonstrated the
capability of QSSA to find the global optima for most of unimodal and multimodal
benchmark functions, especially with complex search space. Moreover, the results revealed
that the proposed image enhancement based QSSA is robust and can distinctly
enhance the contrast of the color images. To the best of our knowledge, this thesis is
first to propose the hybridization of chaos theory with SSA, random walk of l´evy-flight
method with GOA, and the principles of quantum computing with SSA. Additionally,
this thesis is first to apply the hybridization between chaos theory and SSA on feature
selection optimization problem, the hybridization between l´evy-flight method and GOA
on parameter setting optimization problem, and the hybridization between quantum
theory and SSA on contrast enhancement optimization problem.