Yang Yu

The discovery of population interaction with a power law distribution in brain storm optimization

Brain storm optimization (BSO) is a novel evolutionary algorithm which originates from the human brainstorming process. The successful applications of BSO on various problems demonstrate its validity and efficiency. To theoretically analyze the performance of algorithm from the viewpoint of population evolution, the population interaction network (PIN) is used to construct the relationship among individuals in BSO. Four experiments in different dimensions, parameters, combinatorial parameter settings and related algorithms are implemented, respectively. The experimental results indicate the frequency of average degree of BSO meets a power law distribution in the functions with low dimension, which shows the best performance of algorithm among three kinds of dimensions. The parameters of BSO are investigated to find the influence of the population interaction with the power law distribution on the performance of algorithm, and respective parameter can change the relationship among individuals. In addition, the mutual effect among parameters is analyzed to find the best combinatorial result to significantly enhance the performance of BSO. The contrast among BSO, DE and PSO demonstrates a power law distribution is more effective for boosting the population interaction to enhance the performance of algorithm.

CBSO: a memetic brain storm optimization with chaotic local search

Brain storm optimization (BSO) is a newly proposed optimization algorithm inspired by human being brainstorming process. After its appearance, much attention has been paid on and many attempts to improve its performance have been made. The search ability of BSO has been enhanced, but it still suffers from sticking into stagnation during exploitation phase. This paper proposes a novel method which incorporates BSO with chaotic local search (CLS) with the purpose of alleviating this situation. Chaos has properties of randomicity and ergodicity. These properties ensure CLS can explore every state of the search space if the search time duration is long enough. The incorporation of CLS can make BSO break the stagnation and keep the population’s diversity simultaneously, thus realizing a better balance between exploration and exploitation. Twelve chaotic maps are randomly selected for increasing the diversity of the search mechanism. Experimental and statistical results based on 25 benchmark functions demonstrate the superiority of the proposed method.

Chaotic grey wolf optimization

Grey wolf optimization algorithm (GWO) is a recently proposed meta-heuristics and has shown promising performance in solving complex function optimization and engineering problems. To further enrich the search dynamics of GWO, the chaotic local search (CLS) mechanism is incorporated into GWO to enhance the search by taking the properties of ergodicity and randomness of chaotic maps. Twelve different kinds of chaotic maps are investigated to give some insights into the influence of CLS on GWO. Experimental results based on 29 widely used benchmark functions suggest that CLS indeed enables GWO to possess better performance in terms of solution accuracy, solution distribution, and convergence property. Summarized results also reveal that the performance of the resultant chaotic grey wolf optimization (CGWO) algorithm is effected not only by the characteristics of the embedded chaotic map, but also by the landscape of the solved problems.

A Novel Memetic Whale Optimization Algorithm for Optimization.

Whale optimization algorithm (WOA) is a newly proposed search optimization technique which mimics the encircling prey and bubble-net attacking mechanisms of the whale. It has proven to be very competitive in comparison with other state-of-the-art metaheuristics. Nevertheless, the performance of WOA is limited by its monotonous search dynamics, i.e., only the encircling mechanism drives the search which mainly focus the exploration in the landscape. Thus, WOA lacks of the capacity of jumping out the of local optima. To address this problem, this paper propose a memetic whale optimization algorithm (MWOA) by incorporating a chaotic local search into WOA to enhance its exploitation ability. It is expected that MWOA can well balance the global exploration and local exploitation during the search process, thus achieving a better search performance. Forty eight benchmark functions are used to verify the efficiency of MWOA. Experimental results suggest that MWOA can perform better than its competitors in terms of the convergence speed and the solution accuracy.

Galactic Gravitational Search Algorithm for Numerical Optimization

The gravitational search algorithm (GSA) has proven to be a good optimization algorithm to solve various optimization problems. However, due to the lack of exploration capability, it often traps into local optima when dealing with complex problems. Hence its convergence speed will slow down. A clustering-based learning strategy (CLS) has been applied to GSA to alleviate this situation, which is called galactic gravitational search algorithm (GGSA). The CLS firstly divides the GSA into multiple clusters, and then it applies several learning strategies in each cluster and among clusters separately. By using this method, the main weakness of GSA that easily trapping into local optima can be effectively alleviated. The experimental results confirm the superior performance of GGSA in terms of solution quality and convergence in comparison with GSA and other algorithms.

A Pruning Neural Network Model in Credit Classification Analysis

Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs) have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN) and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs) to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

Multiple Chaotic Cuckoo Search Algorithm

Cuckoo search algorithm (CSA) is a nature-inspired meta-heuristic based on the obligate brood parasitic behavior of cuckoo species, and it has shown promising performance in solving optimization problems. Chaotic mechanisms have been incorporated into CSA to utilize the dynamic properties of chaos, aiming to further improve its search performance. However, in the previously proposed chaotic cuckoo search algorithms (CCSA), only one chaotic map is utilized in a single search iteration which limited the exploitation ability of the search. In this study, we consider to utilize multiple chaotic maps simultaneously to perform the local search within the neighborhood of the global best solution found by CSA. To realize this, three kinds of multiple chaotic cuckoo search algorithms (MCCSA) are proposed by incorporating several chaotic maps into the chaotic local search parallelly, randomly or selectively. The performance of MCCSA is verified based on 48 widely used benchmark optimization functions. Experimental results reveal that MCCSAs generally perform better than CCSAs, and the MCCSA-P which parallelly utilizes chaotic maps performs the best among all 16 compared variants of CSAs.