We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multi-dimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N 2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for a two-qubit Grover search algorithm which is a quantum algorithm providing quadratic speedup over the classical counterpart.
In PEAs the whole population forms in a distributed way and consists multiple subpopulation. Single-population master-slaves, multiple populations, fine-grained and hierarchical combinations are the main types of PEAs @cite_28 . The proposed algorithm in this paper differs from the PEAs in that all members of the population are interacting and there is a mutual effect between the members of the population in addition to leaders' effect on individuals in their groups. The sum of all these interactions forms the evolutionary technique. However, in PEAs, in most cases, the interaction between subpopulations is made with the migration of individuals and evolutionary techniques used for subpopulations can be independent from each other.
@cite_28
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Parallel Evolutionary Optimization.- A Model for Parallel Operators in Genetic Algorithms.- Parallel Evolutionary Multiobjective Optimization.- Parallel Hardware for Genetic Algorithms.- A Reconfigurable Parallel Hardware for Genetic Algorithms.- Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms.- Distributed Evolutionary Computation.- Performance of Distributed GAs on DNA Fragment Assembly.- On Parallel Evolutionary Algorithms on the Computational Grid.- Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit.- Parallel Particle Swarm Optimization.- Intelligent Parallel Particle Swarm Optimization Algorithms.- Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model.