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 also investigate the finite time behavior of CRO in respect of convergence rate and first hitting time. / published_or_final_version / Electrical and Electronic Engineering / Doctoral / Doctor of Philosophy

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published_or_final_version / Chemistry / Doctoral / Doctor of Philosophy

Chemical reaction networks are an unconventional computing medium that could benefit from the ability to form basic control systems. In this work, we demonstrate the functionality of a chemical control system by evaluating classic genetic algorithm problems: Koza's Santa Fe trail, Jefferson's John Muir trail, and three Santa Fe trail segments. Both Jefferson and Koza found that memory, such as a recurrent neural network or memories in a genetic program, are required to solve the task. Our approach presents the first instance of a chemical system acting as a control system. We propose a delay line connected with an artificial neural network in a chemical reaction network to determine the artificial ant's moves. We first search for the minimal required delay line size connected to a feed forward neural network in a chemical system. Our experiments show a delay line of length four is sufficient. Next, we used these findings to implement a chemical reaction network with a length four delay line and an artificial neural network. We use genetic algorithms to find an optimal set of weights for the artificial neural network. This chemical system is capable of consuming 100% of the food on a subset and greater than 44% of the food on Koza's Santa Fe trail. We also show the first implementation of a simulated chemical memory in two different models that can reliably capture and store information over time. The ability to store data over time gives rise to basic control systems that can perform more complex tasks. The integration of a memory storage unit and a control system in a chemistry has applications in biomedicine, like smart drug delivery. We show that we can successfully store the information over time and use it to act as a memory for a control system navigating an agent through a maze.