Synchronous and asynchronous Pareto-based multi-objective Artificial Bee Colony algorithms

Pareto-based multi-objective optimization algorithms prefer non-dominated solutions over dominated solutions and maintain as much as possible diversity in the Pareto optimal set to represent the whole Pareto-front. This paper proposes three multi-objective Artificial Bee Colony (ABC) algorithms based on synchronous and asynchronous models using Pareto-dominance and non-dominated sorting: asynchronous multi-objective ABC using only Pareto-dominance rule (A-MOABC/PD), asynchronous multi-objective ABC using non-dominated sorting procedure (A-MOABC/NS) and synchronous multi-objective ABC using non-dominated sorting procedure (S-MOABC/NS). These algorithms were investigated in terms of the inverted generational distance, hypervolume and spread performance metrics, running time, approximation to whole Pareto-front and Pareto-solutions spaces. It was shown that S-MOABC/NS is more scalable and efficient compared to its asynchronous counterpart and more efficient and robust than A-MOABC/PD. An investigation on parameter sensitivity of S-MOABC/NS was presented to relate the behavior of the algorithm to the values of the control parameters. The results of S-MOABC/NS were compared to some state-of-the art algorithms. Results show that S-MOABC/NS can provide good approximations to well distributed and high quality non-dominated fronts and can be used as a promising alternative tool to solve multi-objective problems with the advantage of being simple and employing a few control parameters

Multi-Objective Optimization of Solar Thermal Combisystems

Solar thermal combisystems can significantly reduce primary energy consumption for residential buildings and therefore cut down greenhouse gas emissions; however, the overall performance of such systems depends on their design (i.e., configuration and sizing of their components) and operating conditions. Designing solar thermal combisystems can be improved by using optimization methods. Therefore, this doctoral thesis introduces a multi-objective optimization framework for optimizing the configuration and equipment sizing of solar thermal combisystems. A micro-time variant multiobjective particle swarm optimization (micro-TVMOPSO) algorithm is developed for handling engineering optimization problems, such as the multi-objective optimization of solar combisystems, where evaluating objective functions is time-consuming. The proposed framework uses a generic solar combisystem model coupled with the micro-TVMOPSO algorithm to find a set of optimized combisystem designs. Applied to two case studies, the multi-objective optimization framework was able to find designs reducing the life cycle cost, life cycle energy use, and life cycle exergy destroyed of solar thermal combisystems. The proposed multi-objective optimization framework can therefore be used to get the most out of solar thermal combisystems given specificc economic and environmental conditions