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In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures; that is, to build algorithms to make a wide and efficient explorations of the search space and then gradually make that the best-evaluated search agent to attract the rest of the swarm. Following this general idea, two REAB-based algorithms are proposed; one derived from the PSO and one derived from the GWO, called REAB-PSO and REAB-GWO, respectively. To easily and succinctly express both algorithms, variable-sized open balls are employed. A comparison of proposed procedures in this paper and the original PSO and GWO using a controller tuning problem as a test bench show a significant improvement of the REAB-based algorithms over their original counterparts. Ideas here exposed can be used to derive new swarm intelligence algorithms. [ABSTRACT FROM AUTHOR]

In control research and design it is frequently necessary to explore, evaluate, tune and compare many control strategies. These activities are assisted by software tools of increasing complexity; however, even with the existing high performance tools these activities are very time consuming due to they imply hundred if not thousand of simulations. If the process of doing such simulations is not automated it can be a very time consuming task. There has been proposed evolutionary algorithms (EA) that in the search for an optimal control automatically generate many control structures. However, the space of possible controllers for any dynamical system is huge. Hence it is mandatory to restrict the search space. The best way to restrict the controller search space is to let the designer influence the search direction. In this paper we propose a software tool for control research that has as its main part an EA that produce only controllers having a pre-specified morphology. By specifying a controller morphology the designer can influence the search direction without losing the exploration capability of evolutionary algorithms. The EA is endowed with a cost function tailored for fast evaluation of closed-loop controller performance. The use of the tool is illustrated by searching an sliding mode and similar controllers for an unstable linear and two nonlinear systems. [ABSTRACT FROM AUTHOR]