We have recently proposed two algorithms for continuous optimization that combine aspects of social learning and swarm intelligence. One of these algorithms, called IPSO, is a particle swarm optimizer with a growing population size in which new particles are initialized using a rule that biases their initial random position towards the best so far solution. The other algorithm, called IPSOLS, is an extension of IPSO which allows particles to improve their previous best position by local search.In this paper, we improve the efficiency of IPSOLS by using the local search procedure only when it is expected to be profitable. We assess its performance by comparing it with fixed and growing population size particle swarm optimization algorithms with and without local search as well as with a random restart local search algorithm. Moreover, we measure the contribution of the algorithms' core components to their overall performance on a family of problems with different fitness distance correlations.The results of the conducted experiments and of the analysis of the initialization rule show that an incremental growth of the population size in combination with a local search procedure produces a competitive algorithm that is capable of finding good solutions very rapidly without compromising its global search capabilities on problems with positive fitness distance correlation.

We present an algorithm that is inspired by theoretical and empirical results in social learning and swarm intelligence research. The algorithm is based on a framework that we call incremental social learning. In practical terms, the algorithm is a hybrid between a local search procedure and a particle swarm optimization algorithm with growing population size. The local search procedure provides rapid convergence to good solutions while the particle swarm algorithm enables a comprehensive exploration of the search space. We provide experimental evidence that shows that the algorithm can find good solutions very rapidly without compromising its global search capabilities.