Particle swarm optimization with state-based adaptive velocity limit strategy

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.Comment: 33 pages, 8 figure

Avoiding convergence in cooperative coevolution with novelty search

Cooperative coevolution is an approach for evolving solutions composed of coadapted components. Previous research has shown, however, that cooperative coevolutionary algorithms are biased towards stability: they tend to converge prematurely to equilibrium states, instead of converging to optimal or near-optimal solutions. In single-population evolutionary algorithms, novelty search has been shown capable of avoiding premature convergence to local optima — a pathology similar to convergence to equilibrium states. In this study, we demonstrate how novelty search can be applied to cooperative coevolution by proposing two new algorithms. The first algorithm promotes behavioural novelty at the team level (NS-T), while the second promotes novelty at the individual agent level (NS-I). The proposed algorithms are evaluated in two popular multiagent tasks: predator-prey pursuit and keepaway soccer. An analysis of the explored collaboration space shows that (i) fitnessbased evolution tends to quickly converge to poor equilibrium states, (ii) NS-I almost never reaches any equilibrium state due to constant change in the individual populations, while (iii) NS-T explores a variety of equilibrium states in each evolutionary run and thus significantly outperforms both fitness-based evolution and NS-I.info:eu-repo/semantics/acceptedVersio

A two-level evolution strategy : balancing global and local search

Evolution Strategies apply mutation and recombination operators in order to create their offspring. Both operators have a different role in the evolution process: recombination should combine information of different individuals, while mutation performs a kind of random walk to introduce new values. In an ES these operators are always applied together, but their different roles suggest that it might be better to apply them independently and at different rates. In order to do so the ES has been split into two levels. The resulting Modular Evolution Strategy consists of a population of local optimizers and a distributed population manager. Both parts have their own specific role in the optimization process. As a result of its modularity this method can be adapted more easily to specific classes of numerical optimization problems, and introduction of adaptive mechanisms is relatively easy. A further interesting aspect about this algorithm is that it does not need any global communication, and therefore can be parallelized easily. Many problems can be expressed as numerical optimization problems. Especially when the dimension of the input space and the number of local optima is high these problems tend to be very difficult. In order to obtain an efficient solver one has to gather information regarding the function to be optimized. Evolution based learning can be used to obtain this information. This paper contains results obtained with the Modular Evolution Strategy and compares these results to those obtained with other evolution based method. The results look promising