In this paper we apply the h-generated fuzzy implications to prove a number of results which are of fundamental importance to the theory of fuzzy and vague functional and multivalued dependencies defined on given scheme. Our research is motivated by the fact that some analogous results already hold true for the families of f- and g-generated fuzzy implications, and the fact that these three collections of implications share many similar mutual properties. While some of the aforementioned implications are introduced in order to be applied in approximate reasoning, the results derived in this paper represent the main tool in the process of automation and are also used to complement the resolution principle. More precisely, the main result of this research states that the fact that some fuzzy (vague) relation instance r, |r| = 2, satisfies some fuzzy (vague) functional or fuzzy (vague) multivalued dependency c /∈ C (under assumption that r satisfies some set C of fuzzy (vague) functional and fuzzy (vague) multivalued dependencies), yields that the fuzzy formula attached to c is valid whenever all of the fuzzy formulas attached to the elements of C are valid. What is more important is that the opposite claim is also proven. Its importance stems from the fact that the verification by hand, which means purely theoretical verification, that C implies c is not required anymore. Now, in order to prove that some C yields some c, it is enough to make the use of the resolution principle, and automatically verify whether or not the set of the attached fuzzy formulas yields the fuzzy formula attached to c. In the case of affirmative answer, the desired dependency follows. The research conducted in this paper represent a natural generalization of our previous research since it includes and considers both, fuzzy and vague theories.

Distribution companies often store goods in large warehouses. Orders are collected and prepared for transport. Large-scale warehouses are often divided into sectors. Each worker collects a part of the order from the assigned sector. In that case, workers often pick small orders and the process is not optimal. Therefore, order batching is done, where one worker collects multiple orders at a time. In this paper, an innovative concept of orders batching in a warehouse with a 48-hour delivery based on a metaheuristic approach is described. The algorithm divides each order by sectors. An analysis of each part of the order is done and the possibility of batching based on the order content is checked. The order batching is based on the discrete Bat algorithm. The transport scheme and the order of loading goods into the truck are observed. In the order picking process, a number of standard constraints such as weight and item priorities are considered. The concept has been implemented and tested for 50 days of warehouse operation in one of the largest warehouses in Bosnia and Herzegovina. The algorithm is compared with the earlier approach of collecting orders in the warehouse, and significant progress has been observed in the number of kilometers traveled on a daily basis.

Many public figures, companies and associations are planning events in different cities and at the same time have active profiles on social media. The planning process requires processing a large amount of data and different parameters when choosing the best event venue. Social media captures a large number of fan actions per day. This paper describes the process of selecting the most appropriate cities to organize events, aided by data collected from social media. The problem is defined as a combinatorial optimization problem. A modified metaheuristic Bat algorithm was proposed, implemented, and described in detail to solve the problem. Although the original Bat algorithm is designed to solve continuous optimization problems, the implemented bat algorithm is adapted to solve the defined problem. The algorithm is compared to the exhaustive search method for smaller instances, and to the greedy and genetic algorithm for larger instances. The algorithm was tested on benchmark data on cities in 20 European countries, as well as on real data collected from pages on the social network Facebook. Bat algorithm has shown superior results compared to other techniques, both in time and in the quality of the solutions generated.

It is well-known that determining the optimal number of guards which can cover the interior of a simple nonconvex polygon presents an NP-hard problem. The optimal guard placement can be described as a problem which seeks for the smallest number of guards required to cover every point in a complex environment. In this paper, we propose an exact twophase method as well as an approximate method for tackling the mentioned issue. The proposed exact approach in the first phase maps camera placement problem to the set covering problem, while in the second phase it uses famous state-of-the-art CPLEX solver to address set covering problem. The performance of our combined exact algorithm was compared to the performance of the approximate one. According to the results presented in the experimental analysis, it can be seen that the exact approach outperforms the approximate method for all instances.

To tackle a specific class of engineering problems, in this paper, we propose an effectively integrated bat algorithm with simulated annealing for solving constrained optimization problems. Our proposed method (I-BASA) involves simulated annealing, Gaussian distribution, and a new mutation operator into the simple Bat algorithm to accelerate the search performance as well as to additionally improve the diversification of the whole space. The proposed method performs balancing between the grave exploitation of the Bat algorithm and global exploration of the Simulated annealing. The standard engineering benchmark problems from the literature were considered in the competition between our integrated method and the latest swarm intelligence algorithms in the area of design optimization. The simulations results show that I-BASA produces high-quality solutions as well as a low number of function evaluations.

The planning of concert tours can be a challenging process which requires a large amount of data to be analyzed. The greatest profit cannot be obtained only by maximizing the expected number of visitors. However, most of the organizers mainly focus on that part of planning. To achieve the maximum profit possible, organizers must include other data in their analysis. Social media play a powerful role in music industry. Most of the mentioned data can be found online on social media like Facebook, YouTube or Instagram. Such data can be found in analytic sections of fan or event pages. In this paper, algorithms for tour planning have been introduced by using above mentioned data. Proposed algorithms are based on heuristic methods such as simulated annealing and genetic algorithm. A clustering based method is also implemented. Aforementioned algorithms were tested on real-world instances from Facebook fan page analytics and use number of fans and distance between cities.

Many users need social media platforms to improve business. The usage of those platforms is usually focused on the marketing and customer targeting. Platforms like Facebook, Instagram or YouTube give their users a large number of reports and analytic tools. Public figures and organizations have a large number of followers who generate a significant number of activities. This paper focuses on the use of Facebook's geography analytic in the process of events planning. The problem is formulated as a combinatorial optimization problem. Data from social media platforms are used as an input to nature-inspired optimization algorithm. A public data set has been created with cities from 20 European countries. An adjusted genetic algorithm (AGA) is proposed. The greedy approach and AGA are compared on real data from several Facebook pages and on the created public dataset. The genetic algorithm shows better results and it gives the same solution as an exhaustive search for smaller instances.

A 32-item scale that can be used to measure physics students' understanding of introductory level wave optics.

Single hidden layer feed forward neural networks are widely used for various practical problems. However, the training process for determining synaptic weights of such neural networks can be computationally very expensive. In this paper we propose a new learning algorithm for learning the synaptic weights of the single hidden layer feedforward neural networks in order to reduce the learning time. We propose combining the upgraded bat algorithm with the extreme learning machine. The proposed approach reduces the number of evaluations needed to train a neural network and efficiently finds optimal input weights and the hidden biases. The proposed algorithm was tested on standard benchmark classification problems and functions and compared with other approaches from literature. The results have shown that our approach produces a satisfactory performance in almost all cases and that it can obtains solutions much faster than the traditional learning algorithms.

Data mining and clustering are important elements of various applications in different fields. One of the areas were clustering is rather frequently used is web intelligence, which nowadays represents an important research area. Data collected from the web are usually very complex, dynamic, without structure and rather large. Traditional clustering techniques are not efficient enough and need to be improved. In this paper, we propose combination of recent swarm intelligence algorithm, bare bones fireworks algorithm, and k-means for clustering web intelligence data. The proposed method was compared with other approaches from literature. Based on the experimental results, it can be concluded that the proposed method has very promising characteristics in terms of the quality of clustering, as well as the execution time.