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How to manage trade-off between exploitation and exploration in Particle Swarm Optimization (PSO) for efficiently solving various optimization problems is an important issue. In order to prevent premature convergence in PSO search, this paper proposes a new method, Particle Swarm Optimization with Diversive Curiosity (PSO/DC). A key idea of the proposed method is to introduce a mechanism of diversive curiosity into PSO for preventing premature convergence and for managing the exploration-exploitation trade-off. Diversive curiosity is represented by an internal indicator that detects marginal improvement of a swarm of particles for certain number of iterations, and forces them to continually explore an optimal solution to a given optimization problem. Applications of the proposed method to a 2-dimensional optimization problem well demonstrate its effectiveness. Our experimental results indicate that the performance (100%) by the proposed method is superior in terms of success ratio to that (60%) by the PSO model optimized by EPSO, and basically accord with the finding called "the zone of curiosity" in psychology. [ABSTRACT FROM AUTHOR]

Abstract: In many countries the power systems are going to move toward creating a competitive structure for selling and buying electrical energy. These changes and the numerous advantages of the distributed generation units (DGs) in term of their technology enhancement and economical considerations have created more incentives to use these kinds of generators than before. Therefore, it is necessary to study the impact of DGs on the power systems, especially on the distribution networks. The distribution feeder reconfiguration (DFR) is one of the most important control schemes in the distribution networks, which can be affected by DGs. This paper presents a new approach to DFR at the distribution networks considering DGs. The main objective of the DFR is to minimize the deviation of the bus voltage, the number of switching operations and the total cost of the active power generated by DGs and distribution companies. Since the DFR is a nonlinear optimization problem, we apply the particle swarm optimization (PSO) approach to solve it. The feasibility of the proposed approach is demonstrated and compared with other evolutionary methods such as genetic algorithm (GA), Tabu search (TS) and differential evolution (DE) over a realistic distribution test system. [Copyright &y& Elsevier]