This paper presents a comparison of a differential evolution (DE) algorithm and a modified discrete particle swarm optimization (MDPSO) algorithm for generating optimal preventive maintenance schedules for economical and reliable operation of a power system, while satisfying system load demand and crew constraints. The DE, an evolutionary technique and an optimization algorithm utilizes the differential information to guide its further search, and can handle mixed integer discrete continuous optimization problems. Discrete particle swarm optimization (DPSO) is known to effectively solve large scale multi-objective optimization problems and has been widely applied in power systems. Both the DE and MDPSO are applied to solve a multi-objective generator maintenance scheduling (GMS) optimization problem. The two algorithms generate feasible and optimal solutions and overcome the limitations of the conventional methods including extensive computational effort, which increases exponentially as the size of the problem increases. The proposed methods are tested, validated and compared on the Nigerian power system.

Meeting Name

2008 IEEE Swarm Intelligence Symposium (2008, St. Louis, MO)


Electrical and Computer Engineering


National Science Foundation (U.S.)

Keywords and Phrases

Electric Generators; Particle Swarm Optimisation; Evolutionary Computation; Integer Programming; Power System Management; Power System Measurent; Preventive Maintenance; Scheduling

Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





© 2008 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Sep 2008