Contributions to the development of the CRO-SL algorithmengineering application problems

Abstract

This Ph.D. thesis discusses advanced design issues of the evolutionary-based algorithm "Coral Reef Optimization", in its Substrate-Layer (CRO-SL) version, for optimization problems in Engineering Applications. Evolutionary Algorithms (EAs) have been widely applied to hard optimization problems when analytic approaches are not applicable. These problems usually have huge search spaces, a high number os constrains and unknown or discrete objective functions. While computational complexity of EAs algorithms is moderate, fitness function time executions may also be heavy. The increasing processing capacity in technology opens the door to tackling costly problems through meta-heuristics. One of the drawbacks of using this approaches is that it is not possible to know in advance which of them is the best for a specific problem (No-free lunch Theorem) and, whatever the choice, its application does not ensure to get the optimum solution, given the stochastic nature of the meta-heuristic. That is the reason why we have chosen CRO-SL algorithm, because it allows to combine the most powerful search procedures in a kind of co-evolution competitive approach, helping each other in order to attain the global optimum. By applying the CRO-SL to these problems, we aim to satisfy two main objectives: first, to test the goodness of the CRO-SL algorithm in the selected applications. To do this, alternative state of the art objective meta-heuristics will be compared with the proposed CRO. The second is to promote the use of the CRO-SL as a tool for comparison between exploration methods. Some of the meta-heuristic algorithms are based on the iteration of a search method over a population of codified individuals, who represent solutions of the problem. The CRO-SL borrows the form in which other algorithms change their individuals, and forms new solutions in parallel. Among the best-known EAs included as substrates in the CRO-SL are: Harmony Search, Differential Evolution, Genetic Algorithms. In addition we propose and analyze the use of another type of mutations such as the Gaussian type, single mutation or multipoint crossover. Finally, during the development of this Thesis a new form of search based on strange attractors has also been tested. The problems that can be tackled with meta-heuristic approaches is very wide and varied, and it is not exclusive of engineering. However we focus the Thesis on it area, one of the most prominent in our time. One of the proposed application is battery scheduling problem in MicroGrids (MGs). Specifically, we consider an MG that includes renewable distributed generation and different loads, defined by its power profiles, and is equipped with an energy storage device (battery) to address its programming (duration of loading / discharging and occurrence) in a real scenario with variable electricity prices. Also, we discuss a problem of vibration cancellation over structures of two and four floors, using Tuned Mass Dampers (TMD’s). The optimization algorithm will try to find the best solution by obtaining three physical parameters and the TMD location. As another related application, CRO-SL is used to design Multi-Input-Multi-Output Active Vibration Control (MIMO-AVC) via inertial-mass actuators, for structures subjected to human induced vibration. In this problem, we will optimize the location of each actuator and tune control gains. Finally, we tackle the optimization of a textile modified meanderline Inverted-F Antenna (IFA) with variable width and spacing meander, for RFID systems. Specifically, the CRO-SL is used to obtain an optimal antenna design, with a good bandwidth and radiation pattern, ideal for RFID readers. Radio Frequency Identification (RFID) has become one of the most numerous manufactured devices worldwide due to a reliable and inexpensive means of locating people. They are used in access and money cards and product labels and many other applications.