A hybrid Particle Swarm Evolutionary Algorithm for Constrained Multi-Objective Optimization

In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained multi-objective optimization. Firstly, in order to keep some particles with smaller constraint violations, a threshold value is designed, the updating strategy of particles is revised based on the threshold value; then in order to keep some particles with smaller rank values, an infeasible elitist preservation strategy is proposed in order to make the infeasible elitists act as bridges connecting disconnected feasible regions. Secondly, in order to find a set of diverse and well-distributed Pareto-optimal solutions, a new crowding distance function is designed for bi-objective optimization problems. It can assign larger crowding distance function values not only for the particles located in the sparse region but also for the particles located near to the boundary of the Pareto front. In this step, the reference points are given, and the particles which are near to the reference points are kept no matter how crowded these points are. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the total force is computed first, then it is used as a mutation direction, searching along this direction, better particles will be found. The comparative study shows the proposed algorithm can generate widely spread and uniformly distributed solutions on the entire Pareto front

A Hybrid Evolutionary Algorithm for Efficient Exploration of Online Social Networks

Online social networks provide large amount of valuable data and may serve as research platforms for various social network analysis tools. In this study, we propose a mathematical model for efficient exploration of an online social network. The goal is to spend minimal amount of time searching for characteristics which define a sub-network of users sharing the same interest or having certain common property. We further develop an efficient hybrid method (HEA), based on the combination of an Evolutionary Algorithm (EA) with Local Search procedure (LS). The proposed mathematical model and hybrid method are benchmarked on real-size data set with up to 10 000 users in a considered social network. We provide optimal solutions obtained by CPLEX solver on problem instances with up to 100 users, while larger instances that were out of reach of the CPLEX were efficiently solved by the proposed hybrid method. Presented computational results show that the HEA approach quickly reaches all optimal solutions obtained by CPLEX solver and gives solutions for the largest considered instance in very short CPU time

A hybrid particle swarm evolutionary algorithm for constrained multi-objective optimization

In this paper, a hybrid particle swarm evolutionary algorithm is proposed for solving constrained multi-objective optimization. Firstly, in order to keep some particles with smaller constraint violations, a threshold value is designed, the updating strategy of particles is revised based on the threshold value; then in order to keep some particles with smaller rank values, an infeasible elitist preservation strategy is proposed in order to make the infeasible elitists act as bridges connecting disconnected feasible regions. Secondly, in order to find a set of diverse and welldistributed Pareto-optimal solutions, a new crowding distance function is designed for bi-objective optimization problems. It can assign larger crowding distance function values not only for the particles located in the sparse region but also for the particles located near to the boundary of the Pareto front. In this step, the reference points are given, and the particles which are near to the reference points are kept no matter how crowded these points are. Thirdly, a new mutation operator with two phases is proposed. In the first phase, the total force is computed first, then it is used as a mutation direction, searching along this direction, better particles will be found. The comparative study shows the proposed algorithm can generate widely spread and uniformly distributed solutions on the entire Pareto front