<p style='text-indent:20px;'>This paper proposes a multi-objective lion swarm optimization based on multi-agent (MOMALSO) for solving the increasingly complex problem in engineering practice. First, Multi-agent system is introduced into (LSO) algorithm. The mechanism of LSO and information exchange between agents are integrated to enhance local search global ability algorithm, self-learni...

Particle swarm optimization techniques are typically made up of a population of simple agents interacting locally with one another and with their environment, with the goal of locating the optima within the operational environment. In this paper, a robust and intelligent particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are incorporated ...

Conditions for swarm stability of nonlinear highorder multi-agent systems are analyzed based on the idea of space transformation. Swarm stability can be assured by sufficient connectivity of graph topology and dissipative property regulated by relative Lyapunov function, with two independent variables. The problems addressed are general, since the models concerned can be time-varying or heterog...

This paper studies a parallel version of the Vector Evaluated Particle Swarm Optimization (VEPSO) method for multiobjective problems. Experiments on well known and widely used test problems are performed, aiming at investigating both the efficiency of VEPSO as well as the advantages of the parallel implementation. The obtained results are compared with the corresponding results of the Vector Ev...

Many real world optimization scenarios impose certain limitations, in terms of constraints and bounds, on various factors affecting the problem. In this paper we formulate several methods for bound handling of decision variables involved in solving a multi-objective optimization problem using particle swarm optimization algorithm. We further compare the performance of these methods on different...

This paper describes our submission to the Tuning Task of WMT16. We replace the grid search implemented as part of standard minimum-error rate training (MERT) in the Moses toolkit with a search based on particle swarm optimization (PSO). An older variant of PSO has been previously successfully applied and we now test it in optimizing the Tuning Task model for English-to-Czech translation. We al...