Review: Multi-objective optimization methods and application in energy saving - Cui, Yunfei; Geng, Zhiqiang; Zhu, Qunxiong; Han, Yongming (Energy (Oxford), 2017)
Multi-objective optimization problems are difficult to solve in that the optimized objectives are usually conflicting with each other. It is usually hard to find an optimal solution that satisfies all objectives from the mathematical point of view. Unlike analytical methods and classical numerical methods, which require strict mathematical calculation or defined initial search values, intelligent optimization algorithms are heuristic algorithms able to find global optimal solutions. In this paper, we make a brief introduction of multi-objective optimization problems and some state-of-the-art intelligent algorithms. In order to get the final optimal solution in the real-world multi-objective optimization problems, trade-off methods including a priori methods, interactive methods, Pareto-dominated methods and new dominance methods are utilized. Moreover, we give a review of multi-objective optimization methods application in the environmental protection fields, for optimization objectives of energy saving, emissions reduction and cost reduction, etc. At last, a whole summary about current difficulties existed in the multi-objective optimization problem is given out, serving as suggestions or guidance for future researches. [Display omitted] •This paper makes a systematic review of multi-objective optimization methods.•This paper introduces intelligent algorithms developments and (dis)advantages.•This paper makes a summary of trade-off methods for compromising objectives.•This paper illustrates methods applications in energy and environment issues.