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This dissertation, A Study of Chemical Reaction Optimization by Jin, Xu, 徐进, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Complex optimization problems are prevalent in various fields of science and engineering. However, many of them belong to a category of problems called NP- hard (nondeterministic polynomial-time hard). On the other hand, due to the powerful capability in solving a myriad of complex optimization problems, metaheuristic approaches have attracted great attention in recent decades. Chemical Reaction Optimization (CRO) is a recently developed metaheuristic mimicking the interactions of molecules in a chemical reaction. With the flexible structure and excellent characteristics, CRO can explore the solution space efficiently to identify the optimal or near optimal solution(s) within an acceptable time. Our research not only designs different versions of CRO and applies them to tackle various NP-hard optimization problems, but also investigates theoretical aspects of CRO in terms of convergence and finite time behavior. We first focus on the problem of task scheduling in grid computing, which involves seeking the most efficient strategy for allocating tasks to resources. In addition to Makespan and Flowtime, we also take reliability of resource into account, and task scheduling is formulated as an optimization problem with three objective functions. Then, four different kinds of CRO are designed to solve this problem. Simulation results show that the CRO methods generally perform better than existing methods and performance improvement is especially significant in large-scale applications. Secondly, we study stock portfolio selection, which pertains to deciding how to allocate investments to a number of stocks. Here we adopt the classical Markowitz mean-variance model and consider an additional cardinality constraint. Thus, the stock portfolio optimization becomes a mixed-integer quadratic programming problem. To solve it, we propose a new version of CRO named Super Molecule-based CRO (S-CRO). Computational experiments suggest that S-CRO is superior to canonical CRO in solving this problem. Thirdly, we apply CRO to the short adjacent repeats identification problem (SARIP), which involves detecting the short adjacent repeats shared by multiple DNA sequences. After proving that SARIP is NP-hard, we test CRO with both synthetic and real data, and compare its performance with BASARD, which is the previous best algorithm for this problem. Simulation results show that CRO performs much better than BASARD in terms of computational time and finding the optimal solution. We also propose a parallel version of CRO (named PCRO) with a synchronous communication scheme. To test its efficiency, we employ PCRO to solve the Quadratic Assignment Problem (QAP), which is a classical combinatorial optimization problem. Simulation results show that compared with canonical sequential CRO, PCRO can reduce the computational time as well as improve the quality of the solution for instances of QAP with large sizes. Finally, we perform theoretical analysis on the convergence and finite time behavior of CRO for combinatorial optimization problems. We explore CRO convergence from two aspects, namely, the elementary reactions and the total system energy. Furthermore, we al

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