Multi-objective learning backtracking search algorithm for economic emission dispatch problem

The backtracking search algorithm (BSA) as a novel intelligent optimizer belongs to population-based evolutionary algorithms. In this paper, a multi-objective learning backtracking search algorithm (MOLBSA) is proposed to solve the environmental/economic dispatch (EED) problem. In this algorithm, we design two novel learning strategies: a leader-choosing strategy, which takes a sparse solution from an external archive as leader; a leader-guiding strategy, which updates individuals with the guidance of leader. These two learning strategies have outstanding performance in improving the uniformity and diversity of obtained Pareto front. The extreme solutions, compromise solution and three metrics obtained by MOLBSA are further compared with those of well-known multi-objective optimization algorithms in IEEE 30-bus 6-unit test system and 10-unit test system. Simulation results demonstrate the capability of MOLBSA in generating well-distributed and high-quality approximation of true Pareto front for the EED problem.

Automated summary

The paper introduces a multi-objective learning backtracking search algorithm (MOLBSA) to solve the environmental/economic dispatch (EED) problem, with two novel learning strategies designed: leader-choosing strategy and leader-guiding strategy. Simulation results demonstrate the capability of MOLBSA in generating well-distributed and high-quality approximation of true Pareto front for the EED problem.