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DEEPSO publications
Papers describing the applicarion of DEEPSO
PMAPS 2014, Durham, UK - DEEPSO and FACTS location Copyright IEEE
This paper presents a new stochastic programming model for PAR/PST definition and location in a network with a high penetration of wind power, with probabilistic representation, to maximize wind power penetration. It also presents a new optimization meta-heuristic, denoted DEEPSO, which is a variant of EPSO, the Evolutionary Particle Swarm Optimization method, borrowing the concept of rough gradient from Differential Evolution algorithms. A test case is solved in an IEEE test system. The performance of DEEPSO is shown to be superior to EPSO in this complex problem.
BRICS-CCI, Porto de Galinhas (PE), Brazil, 8-11 September 2013 Copyright IEEE
This paper explores, with numerical case studies, the performance of an optimization algorithm that is a variant of EPSO, the Evolutionary Particle Swarm Optimization method. EPSO is already a hybrid approach that may be seen as a PSO with self-adaptive weights or an Evolutionary Programming approach with a self-adaptive recombination operator. The new hybrid DEEPSO retains the self-adaptive properties of EPSO but borrows the concept of rough gradient from Differential Evolution algorithms. The performance of DEEPSO is compared to a well-performing EPSO algorithm in the optimization of problems of the fixed cost type, showing consistently better results in the cases presented.
IEEE Swarm Optimization Sysmposium, Indianapolis (Indiana), USA, May 2006
This paper presents some new ideas to improve the performance of EPSO (Evolutionary Particle Swarm Optimization). It discusses a Stochastic Star communication scheme and differential dEPSO. The paper presents results in a didactic Unit Commitment/Generator Scheduling Power System problem and results of a competition among algorithms in an intelligent agent platform for Energy Retail Market simulation where EPSO comes out as the winner algorithm. [this is not DEEPSO but a preliminary hybrid of DE with EPSO - interesting, though]
