Particle Swarm Optimization (PSO) algorithm is a stochastic search technique, which has exhibited good performance across a wide range of applications. However, very often for multi-modal problems involving high dimensions the algorithm tends to suffer from premature convergence. Premature convergence could make the PSO algorithm very difficult to arrive at the global optimum or even a local op...

To overcome the problem of premature convergence on Particle Swarm Optimization (PSO), this paper proposes both the improved particle swarm optimization methods (IPSO) based on self-adaptive regulation strategy and the Chaos Theory. Given the effective balance of particles’ searching and development ability, the self-adaptive regulation strategy is employed to optimize the inertia weight. To im...

introduction: preterm birth is still a major health problem throughout the world, which results in 75% of neonatal mortality. preterm labor not only inflicts financial and emotional distress, it may also lead to permanent disability. the present study was conducted to determine the related risk factors and preventive measures of preterm labor. methods: this retrospective cross-sectional study a...

This paper presents an efficient evolutionary method to optimize the gain ripple of multi-pumps photonic crystal fiber Raman amplifier using the Fuzzy Adaptive Modified PSO (FAMPSO) algorithm. The original PSO has difficulties in premature convergence, performance and the diversity loss in optimization as well as appropriate tuning of its parameters. The feasibility and effectiveness of the pro...

Artificial Bee Colony algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. This paper presents Bee Swarm Optimization intended to introduce chaotic sequences and psychology factor of emotion into the algorithm. We define two emotions Bees could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature c...

Persistent CAD algorithms offer the potential to optimize power consumption for programmable chips post development and deployment. The basic idea is that algorithms continue to search for better design solutions and as better solutions are found, the design is deployed to programmable chips resulting in better performance. In this work, we further study a persistent placement algorithm for FPG...

This paper presents a modification of the ant colony optimization algorithm (ACO) intended to introduce psychology factor of emotion into the algorithm. We define only two emotions ants could have, positive and negative, and correspond to two reaction to perception respectively. For avoiding premature convergence allows Emotional Ant Colony Optimization (EACO) to continue search for better even...

cellular manufacturing system is one of the most important applications of group technology. design of this system involves many structural and operational issues, in which the cell formation and production planning are two important steps. in this paper, a new mathematical model is proposed for integration of cell formation and production planning problems with the aim of minimizing the overal...

Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting...