The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Le´vy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95 confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95 confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90 confidence level.
@cite_0 adopted the cuckoo search algorithm (CS), which mimics the unique lifestyle and aggressive reproduction strategy of the cuckoo. First, the CS generates random initial eggs in host nests. Each egg in a nest represents a vector solution that represents a test case. In each generation, two operations are performed. Initially, a new nest is generated (typically through a Lévy flight) and compared with the existing nests. The new nest replaces the current nest if it has a better objective function. Then, the CS adopts probabilistic elitism to maintain the elite solutions for the next generation.
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In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research. The eXtensible Markup Language (XML) is a recommendation of the World Wide Web Consortium (W3C). It is a public format and has been widely adopted as a means of interchanging information among computer programs. With XML documents being typically large, we need to have ways of improving their ease of use and maintainability by keeping their complexity low. This research focused on different ways of determining the complexity of XML documents based on various syntactic and structural aspects of these documents. An XML document represents a generic tree. XML documents are pre-order traversal of equivalent XML trees. One of the important findings was that documents with higher nesting levels had more weights and could therefore be viewed as being more complicated as compared to the documents with lower nesting levels. Another important finding was related to document type definitions (DTDs). DTDs can be expressed as regular expressions providing means for calculating quantitative values. Leptothorax unifasciatus ant colonies occupy flat crevices in rocks in which their brood is kept in a single cluster. In artificial nests made from two glass plates sandwiched together, designed to mimic the general proportions of their nest sites in the field, such colonies arrange their brood in a distinct pattern. These patterns may influence the priority with which different brood are tended, and may therefore influence both the division of labour and colony demography. Different brood stages are arranged in concentric rings in a single cluster centred around the eggs and micro-larvae. Successively larger larvae are arranged in progressive bands away from the centre of the brood cluster. However, the largest and oldest brood items, the prepupae and pupae, are placed in an intermediate position between the largest and most peripheral larvae and the larvae of medium size. Dirichlet tessellations are used to analyze these patterns and show that the tile areas, the area closer to each item than its neighbours, allocated to each type of item increase with distance from the centre of the brood cluster. There is a significant positive correlation between such tile areas and the estimated metabolic rates of each type of brood item. The ants may be creating a “domain of care” around each brood item proportional to that item's needs. If nurse workers tend to move to the brood item whose tile they happen to be within when they have care to donate, they may apportion such care according to the needs of each type of brood. When colonies emigrate to new nests they rapidly recreate these characteristic brood patterns. Multiclass problems, i.e., classification problems involving more than two classes, are a common scenario in supervised classification. An important approach to solve this type of problems consists in using binary classifiers repeated times; within this category we find nested dichotomies. However, most of the methods for building nested dichotomies use a random strategy, which does not guarantee finding a good one. In this work, we propose new non-random methods for building nested dichotomies, using the idea of reducing misclassification errors by separating in the higher levels those classes that are easier to separate; and, in the lower levels those classes that are more difficult to separate. In order to evaluate the performance of the proposed methods, we compare them against methods that randomly build nested dichotomies, using some datasets (with mixed data) taken from the UCI repository.