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-This paper proposes a dynamic ring architecture of interaction among members of population in a Quantum Evolutionary Algorithms (QEA). The ring is allowed to expand/collapse based on a sinusoidal population size and partial re-initialization of new members in the population. The study shows that the ring structure can be an efficient architecture for an effective Exploration/Exploitation tradeoff, and the partial reinitialization of the proposed algorithm can improve the diversity of the algorithm. The proposed approach is tested on Knapsack Problem, Trap Problem as well as 14 numerical optimization functions. Experimental results show that the proposed Structure consistently improves the performance of QEA.

This paper proposes a novel optimization algorithm called Cellular Probabilistic Optimization Algorithms (CPOA) based on the probabilistic representation of solutions for real coded problems. In place of binary integers, the basic unit of information here is a probability density function. This probabilistic coding allows superposition of states for a more efficient algorithm. This probabilistic representation enables the algorithm to climb the hills in the search space. Furthermore, the cellular structure of the proposed algorithm aims to provide an appropriate tradeoff between exploitation and exploration. The proposed algorithm is tested on several numeric benchmark function optimization problems. Experimental results show that the performance of CPEA is improved when compared with other evolutionary algorithms like Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Furthermore, this improvement becomes particularly more significant for problems with higher dimension.

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