الفهرس 
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Abstract
This thesis is aimed at the research of swam intelligent techniques combining and improving modern artificial intelligent algorithms for solving some of combinatorial optimization problems such as linear assignment problem, integer programming and Sudoku puzzles.
This thesis introduced an improved Harmony Search algorithm integrated with chaos to update HM. The HM members were fine-tuned by the chaos operator to improve their affinities. Several examples have been used to prove the effectiveness of the proposed method.
The results have proved a complete match between the proposed technique and the previously used methods with a remarkable improvement in computational time and mathematical working out.
The proposed algorithm managed to solve large scale problems that traditional method could not solve due to exponential growth in time and space complexities. The solution procedure will not face the same time waste in going through non-converging iterations as traditional methods do.
A new hybrid optimization method called improved Flower Pollination Algorithm with Chaotic Harmony Search (FPCHS) is proposed.
The method combines the standard Flower Pollination algorithm (FP) with the chaotic Harmony Search (HS) algorithm to improve the searching accuracy.
The FPCHS algorithm is used to solve Sudoku puzzles. Numerical results show that the FPCHS is accurate and efficient in comparison with standard Harmony Search, (HS) algorithm.
A New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems, Improved Chaotic Bat Algorithm for Solving Integer Programming Problems and an Improved Flower Pollination Algorithm with Chaos are also introduced. Test problems have been used to prove the effectiveness of the proposed algorithms.
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This thesis contains six chapters.
Chapter 1, introduces a survey on optimization, Combinatorial Optimization problems, a discussion on different artificial intelligent optimization solution Algorithms and finally, explores ”Nature-Inspired Meta-Heuristics” algorithms.
Chapter 2, introduces a survey on harmony search algorithm, its improvements and applications in many fields such as operations research, computer science, engineering and economy. the survey ended with a list of recommendations to how get benefit from this vital technique.
Chapter 3, presents an improved version of a Harmony Meta-heuristic Algorithm, (IHSCH), for solving the linear assignment problem. The proposed algorithm uses chaotic behavior to generate a candidate solution in a behavior similar to acoustic monophony. Numerical results show that the IHSCH is able to obtain the optimal results in comparison with traditional methods (the Hungarian Method) and other harmony search algorithms. However, the benefits of this proposed algorithm is its ability to obtain the optimal solution within less computation in comparison with the Hungarian Method.
Harmony search (HS) has proved to be a powerful tool for solving several optimization problems .In chapter 2, we briefly describe the basic algorithm (HS) which the proposed algorithm is based upon.
Chapter 4, presents a new hybrid optimization method called improved Flower Pollination Algorithm with Chaotic Harmony Search (FPCHS) is proposed. The method combines the standard Flower Pollination algorithm (FPA) with the chaotic Harmony Search (HS) algorithm to improve the searching accuracy. The FPCHS algorithm is used to solve Sudoku puzzles. Numerical results show that the FPCHS is accurate and efficient in comparison with standard Harmony Search, (HS) algorithm.
Chapter 5, presents some improvement carried out on the proposed algorithms to enhance efficiency. A variety of testing domains which the new algorithms were tested on together with other algorithms for comparison are also introduced.
A new hybrid optimization method called hybrid flower pollination algorithm (FPPSO) is proposed. The method combines the standard flower pollination algorithm (FPA) with the particle swarm optimization (PSO) algorithm to improve the searching accuracy. The FPPSO algorithm is used to solve constrained optimization problems. Experimental results showed that the accuracy of finding the best solution and convergence speed performance of the proposed algorithm is significantly better compared to those achieved by the existing algorithms.
A new method is developed based on the flower pollination algorithm combined with chaos theory (IFPCH) to solve definite integral. The definite integral has wide ranging applications in operation research, computer science, mathematics, mechanics, physics, and civil and mechanical engineering. Definite integral has always been useful in biostatistics to evaluate distribution functions and other quantities. Numerical simulation results show that the algorithm offers an effective way to calculate numerical value of definite integrals, and it has a high convergence rate, high accuracy and robustness.
This chapter also introduces an improved version of a Bat Meta-heuristic Algorithm, (IBACH), for solving integer programming problems. The proposed algorithm uses chaotic behaviour to generate a candidate solution in behaviours similar to acoustic monophony. Numerical results show that the IBACH is able to obtain the optimal results in comparison to traditional methods (branch and bound), particle swarm optimization algorithm (PSO), standard Bat algorithm and other harmony search algorithms.
However, the benefits of this proposed algorithm is in its ability to obtain the optimal solution within less computation, which save time in comparison with the branch and bound algorithm (exact solution method).
Last chapter, summarizes the research work done in this thesis and suggest directions for further research.