Parallel dependable multi-population differential evolutionary particle swarm optimization for on-line optimal operational planning of energy plants
Norihiro Nishimura, Yoshikazu Fukuyama, Tetsuro Matsui · 2017
This paper presents dependable parallel multi-population differential evolutionary particle swarm optimization (DEEPSO) for on-line optimal operational planning of energy plants. The problem can be formulated as a mixed integer nonlinear optimization problem (MINLP). Since Optimal operational planning of numbers of energy plants are calculated simultaneously in a data center, the problem is required to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for this challenge is speeding up by parallel and distributed processing (PDP). However, PDP utilizes numbers of processes, and countermeasures for various faults of the processes should be considered. The problem requires successive calculation at every control interval for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality are required even if some of the calculation results cannot be returned from distributed processes. Multi-population based evolutionary computation methods have been verified that they can improve solution quality. Using the proposed dependable parallel multi-population DEEPSO based method, it is observed that calculation time becomes about 4.3 times faster than the conventional sequential DEEPSO, and appropriate solution quality can be kept even if some of the calculation results cannot be returned from distributed processes.