Dongdong Yang

Teaching–learning-based optimization with dynamic group strategy for global optimization

Abstract Global optimization remains one of the most challenging tasks for evolutionary computation and swarm intelligence. In recent years, there have been some significant developments in these areas regarding the solution of global optimization problems. In this paper, we propose an improved teaching–learning-based optimization (TLBO) algorithm with dynamic group strategy (DGS) for global optimization problems. Different to the original TLBO algorithm, DGSTLBO enables each learner to learn from the mean of his corresponding group, rather than the mean of the class, in the teacher phase. Furthermore, each learner employs the random learning strategy or the quantum-behaved learning strategy in his corresponding group in the learner phase. Regrouping occurs dynamically after a certain number of generations, helping to maintain the diversity of the population and discourage premature convergence. To verify the feasibility and effectiveness of the proposed algorithm, experiments are conducted on 18 numerical benchmark functions in 10, 30, and 50 dimensions. The results show that the proposed DGSTLBO algorithm is an effective method for global optimization problems.

Artificial immune multi-objective SAR image segmentation with fused complementary features

Artificial immune systems (AIS) are the computational systems inspired by the principles and processes of the vertebrate immune system. AIS-based algorithms typically mimic the human immune systems characteristics of learning and adaptability to solve some complicated problems. Here, an artificial immune multi-objective optimization framework is formulated and applied to synthetic aperture radar (SAR) image segmentation. The important innovations of the framework are listed as follows: (1) an efficient and robust immune, multi-objective optimization algorithm is proposed, which has the features of adaptive rank clones and diversity maintenance by K-nearest-neighbor list; (2) besides, two conflicting, fuzzy clustering validity indices are incorporated into this framework and optimized simultaneously and (3) moreover, an effective, fused feature set for texture representation and discrimination is constructed and researched, which utilizes both the Gabor filters ability to precisely extract texture features in low- and mid-frequency components and the gray level co-occurrence probabilitys (GLCP) ability to measure information in high-frequency. Two experiments with synthetic texture images and SAR images are implemented to evaluate the performance of the proposed framework in comparison with other five clustering algorithms: fuzzy C-means (FCM), single-objective genetic algorithm (SOGA), self-organizing map (SOM), wavelet-domain hidden Markov models (HMTseg), and spectral clustering ensemble (SCE). Experimental results show the proposed framework has obtained the better performance in segmenting SAR images than other five algorithms and behaves insensitive to the speckle noise.

Bag-of-Visual-Words Based on Clonal Selection Algorithm for SAR Image Classification

Synthetic aperture radar (SAR) image classification involves two crucial issues: suitable feature representation technique and effective pattern classification methodology. Here, we concentrate on the first issue. By exploiting a famous image feature processing strategy, Bag-of-Visual-Words (BOV) in image semantic analysis and the artificial immune systems (AIS)s abilities of learning and adaptability to solve complicated problems, we present a novel and effective image representation method for SAR image classification. In BOV, an effective fused feature sets for local feature representation are first formulated, which are viewed as the low-level features in it. After that, clonal selection algorithm (CSA) in AIS is introduced to optimize the prediction error of k-fold cross-validation for getting more suitable visual words from the low-level features. Finally, the BOV features are represented by the learned visual words for subsequent pattern classification. Compared with the other four algorithms, the proposed algorithm obtains more satisfactory and cogent classification experimental results.

An improved teaching-learning-based optimization with neighborhood search for applications of ANN

Teaching-learning-based optimization (TLBO) algorithm, which simulates the teaching-learning process of the class room, is one of the recently proposed swarm intelligent (SI) algorithms. The performance of TLBO is maintained by the teaching and learning process, but when the learners cannot found a better position than the old one at some successive iteration, the population might be trapped into local optima. In this paper, an improved teaching-learning-based optimization algorithm with neighborhood search (NSTLBO) is presented. In the proposed method, a ring neighborhood topology is introduced into the original TLBO algorithm to maintain the exploration ability of the population. Different than the traditional method to utilize the global information, the mutation of each learner is now restricted within a certain neighboring area so as to fully utilize the whole space and avoid over-congestion around local optima. Moreover, a mutation operation is presented to NSTLBO during the duplicate eliminations in order to maintain the diversity of population. To verify the performance of the proposed algorithm, thirty-two benchmark functions are utilized. Finally, three application problems of artificial neural network are examined. The results in thirty-two benchmark functions and three applications of ANN indicate that the proposed algorithm has shown interesting outcomes.

ADAPTIVE RANKS CLONE AND k-NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI-OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune systems characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.

Adaptive multi‐objective optimization based on nondominated solutions

An adaptive hybrid model (AHM) based on nondominated solutions is presented in this study for multi‐objective optimization problems (MOPs). In this model, three search phases are devised according to the number of nondominated solutions in the current population: 1) emphasizing the dominated solutions when the population contains very few nondominated solutions; 2) maintaining the balance between nondominated and dominated solutions when nondominated ones become more; 3) when the population consists of adequate nondominated solutions, dominated ones could be ignored and the isolated nondominated ones are allocated more computational budget by their crowding distance values for heuristic search. To exploit local information efficiently, a local incremental search algorithm, LISA, is proposed and merged into the model. This model maintains the adaptive mechanism between the optimization process by the online discovered nondominated solutions. The proposed model is validated using five ZDT and five DTLZ problems. Compared with three other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, and PESA‐II, AHM achieves comparable results in terms of convergence and diversity metrics. Finally, the sensitivity of introduced parameters and scalability to the number of objectives are investigated.

A hybridization of teaching---learning-based optimization and differential evolution for chaotic time series prediction

Chaotic time series prediction problems have some very interesting properties and their prediction has received increasing interest in the recent years. Prediction of chaotic time series based on the phase space reconstruction theory has been applied in many research fields. It is well known that prediction of a chaotic system is a nonlinear, multivariable and multimodal optimization problem for which global optimization techniques are required in order to avoid local optima. In this paper, a new hybrid algorithm named teaching–learning-based optimization (TLBO)–differential evolution (DE), which integrates TLBO and DE, is proposed to solve chaotic time series prediction. DE is incorporated into update the previous best positions of individuals to force TLBO jump out of stagnation, because of its strong searching ability. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. To demonstrate the effectiveness of our approaches, ten benchmark functions and three typical chaotic nonlinear time series prediction problems are used for simulating. Conducted experiments indicate that the TLBO–DE performs significantly better than, or at least comparable to, TLBO and some other algorithms.

Optimal approximation of stable linear systems with a novel and efficient optimization algorithm

Optimal approximation of linear system models is an important task in the controller design and simulation for complex dynamic systems. In this paper, we put forward a novel nature-based meta-heuristic method, called artificial raindrop algorithm, which is inspired from the phenomenon of natural rainfall, and apply it for optimal approximation of a stable linear system. It mimics the changing process of a raindrop, including the generation of raindrop, the descent of raindrop, the collision of raindrop, the flowing of raindrop and the updating of raindrop. Five corresponding operators are designed in the algorithm. Numerical experiment is carried on the optimal approximation of a typical stable linear system in two fixed search intervals. The result demonstrates better performance of the proposed algorithm comparing with that of other five state-of-the-art optimization algorithms.

A Memetic Particle Swarm Optimization Algorithm for Community Detection in Complex Networks

In recent years, community detection has become a hot research topic in complex networks. Many of the proposed algorithms are for detecting community based on the modularity Q. However, there is a resolution limit problem in modularity optimization methods. In order to detect the community structure more effectively, a memetic particle swarm optimization algorithm (MPSOA) is proposed to optimize the modularity density by introducing particle swarm optimization-based global search operator and tabu local search operator, which is useful to keep a balance between diversity and convergence. For comparison purposes, two state-of-the-art algorithms, namely, meme-net and fast modularity, are carried on the synthetic networks and other four real-world network problems. The obtained experiment results show that the proposed MPSOA is an efficient heuristic approach for the community detection problems.

An efficient automatic SAR image segmentation framework in AIS using kernel clustering index and histogram statistics

Artificial immune system (AIS) has been proven effective for pattern classification by its characteristics of learning and adaptability from the vertebrate immune system. However, little focus has been placed on the synthetic aperture radar (SAR) image segmentation by AIS. In this paper, we present an efficient automatic framework in AIS for SAR image segmentation. It aims at simultaneously solving the following three different crucial issues: (1) the automatic ability of searching true number of land-cover in SAR images; (2) the objective functions in guiding the segmentation of the images with complicated multiplicative noises; and (3) reduction of the computational complexity of segmenting SAR images with large sizes. By the proposed framework here, it can mitigate the above difficulties to a certain degree. Furthermore, a reasonable spatial filtering and watershed transformation are employed in the initial stage of the framework, and then a novel clustering index in Gaussian kernel is designed to lead the searching process, which is beneficial to find the partitions for the clustering problem with highly overlapping and contaminating samples. Besides, we also propose an efficient computing paradigm in AIS with variable length of chromosomes to search the optimal partitions of SAR images, which can find the optimal numbers of clusters automatically. Finally, in order to speed up the segmentation process of SAR image, we employ the histogram statistics to implement the pixels partition; therefore, the segmenting time is dependent on the small number of gray-levels, not the great amounts of whole image pixels. To test the segmentation performances of the proposed algorithm, a detailed experimental analysis was conducted on two simulated SAR images and four complicated real ones. Other four state-of-the-art image segmentation methods are employed for comparison, which are genetic clustering by variable string length encoding (VGA), fast generalized fuzzy C-means clustering (FGFCM), fuzzy local information C-means clustering algorithm (FLICM) and graph partitioning method: spectral clustering ensemble (SCE).