An Optimization Algorithm Based on Brainstorming Process

In this paper, the human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc and or De, which points at one direction for future research on BSO algorithms.  Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. a lot of evolutionary algorithms out there in the literature. The most popular evolutionary algorithms are evolutionary programming (Fogel, 1962), genetic algorithm (Holland, 1975), evolution strategy (Rechenberg, 1973), and genetic programming (Koza, 1992), which were inspired by biological evolution. In evolutionary algorithms, population of individuals survives into the next iteration. Which individual has higher probability to survive is proportional to its fitness value according to some evaluation function. The survived individuals are then updated by utilizing evolutionary operators such as crossover operator and mutation operator, etc. In evolutionary programming and evolution strategy, only the mutation operation is employed, while in genetic algorithms and genetic programming, both the mutation operation and crossover operation are employed. The optimization problems to be optimized by evolutionary algorithms do not need to be mathematically represented as continuous and differentiable functions, they can be represented in any form. Only requirement for representing optimization problems is that each individual can be evaluated as a value called fitness value. Therefore, evolutionary algorithms can be applied to solve more general optimization problems, especially those that are very difficult, if not impossible, for traditional hill-climbing algorithms to solve. Recently, another kind of algorithms, called swarm intelligence algorithms, is attracting more and more attentions from researchers. Swarm intelligence algorithms are usually nature-inspired optimization algorithms instead of evolution-inspired optimization algorithms such as evolutionary algorithms. Similar to evolutionary algorithms, a swarm intelligence algorithm is also a population-based optimization algorithm. Different from the evolutionary algorithms, each individual in a swarm intelligence algorithm represents a simple object such as ant, bird, fish, etc. So far, a lot of swarm intelligence algorithms have been proposed and studied. Among them are particle swarm optimization(PSO) (Eberhart & Shi, 2007; Shi & Eberhart, 1998), ant colony optimization algorithm(ACO) (Dorigo, Maniezzo, & Colorni, 1996), bacterial forging optimization algorithm(BFO) (Passino, 2010), firefly optimization algorithm (FFO) (Yang, 2008), bee colony optimization algorithm (BCO) (Tovey, 2004), artificial immune system (AIS) (de Castro & Von Zuben, 1999), fish school search optimization algorithm(FSO) (Bastos-Filho, De Lima Neto, Lins, Nascimento, & Lima, 2008), shuffled frog-leaping algorithm (SFL) (Eusuff & Lansey, 2006), intelligent water drops algorithm (IWD) (Shah-Hosseini, 2009), to just name a few. In a swarm intelligence algorithm, an individual represents a simple object such as birds in PSO, ants in ACO, bacteria in BFO, etc. These simple objects cooperate and compete among themselves to have a high tendency to move toward better and better search areas. As a consequence, it is the collective behavior of all individuals that makes a swarm intelligence algorithm to be effective in problem optimization. For example, in PSO, each particle (individual) is associated with a velocity. The velocity of each particle is dynamically updated according to its own historical best performance and its companions’ historical best performance. All the particles in the PSO population fly through the solution space in the hope that particles will fly towards better and better search areas with high probability. Mathematically, the updating process of the population of individuals over iterations can be looked as a mapping process from one population of individuals to another population of individuals from one iteration to the next iteration, which can be represented as Pt+1 = f(Pt), where Pt is the population of individuals at the iteration t, f() is the mapping function. Different evolutionary algorithm or swarm intelligence algorithm has a different mapping function. Through the mapping function, we expect the population of individuals will update to better and better solutions over iterations. Therefore mapping functions should possess the property of convergence. For nonlinear and complicated problems, mapping functions more 26 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the publisher's webpage: www.igi-global.com/article/optimization-algorithm-basedbrainstorming-process/62645