Nasibeh Rady Raz - Department of Artificial Intelligent, Islamic Azad University, Mashhad Branch, Mashhad
Mohammad-R Akbarzadeh –T - Center of Excellence on Soft Computing and Intelligent Information Processing, Departments of Electrical &Computer Engineering, Ferdowsi University of Mashhad, Iran

Considerable number of studies confirm a remarkable performance for evolutionary algorithms (EAs); however, this performance deteriorates as EAs face large scale non-separableproblems. This paper proposes a new collaborative approach based on a co-evolutionary framework for solving large scale nonseparableproblems. Here, collaboration means using more interactive and intelligent particles in a search space for faster butnot premature convergence. Proposed ideas for Collaborative CoevolutionaryParticle Swarm Optimization (CLCPSO) are summarized in the following three items. First is adding Skepticism parameter” to redistribute particles with Cauchy and Gaussian distributions, when the algorithm for two sequential runsshows same result. Second is adaptively tuning group diversity to overcome the problem of trapping in local optima, by adding anew particle called Group energizer to call for random topology when the best fit particle has reached a certain age. Third is using a pseudo random number instead of random number for population initialization. Results show that these techniques improve the convergence issue. Application to several CEC2010benchmarks and comparison against several state-of-the-art approaches such as cooperative co-volutionary particle swarm optimization (CCPSO) confirm the merits of the approach.

Cooperative Co-evolution, Collaboration, Large Scale Optimization, Particle Swarm Optimization