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Issue Date: 1-Dec-2013
Title: Leadership based multi-objective optimization with applications in energy systems.
Authors: Bourennani, Farid
Publisher : UOIT
Degree : Doctor of Philosophy (PhD)
Department : Electrical and Computer Engineering
Supervisor : Rahnamayan, Shahryar
Naterer, Greg F.
Keywords: Multi-objective optimization
Multiobjective evolutionary computation
Hybrid energy systems design
Abstract: Multi-objective optimization metaheuristics (MOMs) are powerful methods for solving complex optimization problems but can require a large number of function evaluations to find optimal solutions. Thus, an efficient multi-objective optimization method should generate accurate and diverse solutions in a timely manner. Improving MOMs convergence speed is an important and challenging research problem which is the scope of this thesis. This thesis conducted the most comprehensive comparative study ever in MOMs. Based on the results, multi-objective (MO) versions of particle swarm optimization (PSO) and differential evolution (DE) algorithms achieved the highest performances; therefore, these two MOMs have been selected as bases for further acceleration in this thesis. To accelerate the selected MOMs, this work focuses on the incorporation of leadership concept to MO variants of DE and PSO algorithms. Two complex case studies of MO design of renewable energy systems are proposed to demonstrate the efficiency of the proposed MOMs. This thesis proposes three new MOMs, namely, leader and speed constraint multi-objective PSO (LSMPSO), opposition-based third evolution step of generalized DE (OGDE3), and multi-objective DE with leadership enhancement (MODEL) which are compared with seven state-of-the-art MOMS using various benchmark problems. LSMPSO was found to be the fastest MOM for the problem undertaken. Further, LSMPSO achieved the highest solutions accuracy for optimal design of a photovoltaic farm in Toronto area. OGDE3 is the first successful application of OBL to a MOM with single population (no-coevolution) using leadership and self-adaptive concepts; the convergence speed of OGDE3 outperformed the other MOMs for the problems solved. MODEL embodies leadership concept into mutation operator of GDE3 algorithm. MODEL achieved the highest accuracy for the 30 studied benchmark problems. Furthermore, MODEL achieved the highest solution accuracy for a MO optimization problem of hydrogen infrastructures design across the province Ontario between 2008 and 2025 considering electricity infrastructure constraints.
Appears in Collections:Electronic Theses and Dissertations (Public)
Faculty of Engineering and Applied Science - Doctoral Dissertations

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