Multimodal problems are related to locating multiple, redundant global optima, as opposed to single solution. In practice, generally in engineering problems it is desired to obtain many redundant solutions instead of single global optima since the available resources cannot be enough or not possible to implement the solution in real-life. Hence, as a toolbox for finding multimodal solutions, modified single objective algorithms can able to use. As one of the fundamental modification, from one of the niching schemes, crowding method was applied to Differential Evolution (DE) algorithm to solve multimodal problems and frequently preferred to compared with developed methods. Therefore, in this study, eight different DE are considered/evaluated on ten benchmark problems to provide best possible DE algorithm for crowding operation. In conclusion, the results show that the time varying scale mutation DE algorithm outperforms against other DE algorithms on benchmark problems.

Today, soft computing is a field that is used a lot in solving real-world problems, such as problems in economics, finance, banking... With the aim to serve for solving the real problem, many new theories and/or tools which were proposed, improved to help soft computing used more efficiently. We can mention some theories as fuzzy sets theory (L. Zadeh, 1965), intuitionistic fuzzy set (K Atanasov, 1986). In this paper, we introduce a new notion of support-intuitionistic fuzzy (SIF) set, which is the combination a intuitionistic fuzzy set with a fuzzy set. So, SIF set is a directly extension of fuzzy set and intuitionistic fuzzy sets (Atanassov). Then, we define some operators on support-intuitionistic fuzzy sets, and investigate some properties of these operators.

Ethical filtering has been a painful and controversial issue seen by different angles worldwide. Stalwarts for freedom find newer methods to circumvent banned URLs while generative power of the Internet outpaces velocity of censorship. Hence, keeping online content safe from anti-religious and sexually provocative content is a growing issue in conservative countries in Asia and The Middle East. Solutions for online ethical filters are linearly upper bound given computation and big data growth scales. In this scenario, Semantic Vectors are applied as automatic ethical filters to calculate accuracy and efficiency metrics. The results show a normative tag cloud generated with superior performance to industry solutions.

Convergence of information and communication technology has brought a radical change in the way data are collected or generated for ease of multi criterion decision making. The huge data is of no use unless it provides certain information. It is very tedious to select a best option among an array of alternatives. Also, it becomes more tedious when the data contains uncertainties and objectives of evaluation vary in importance and scope. Unlocking the hidden data is of no use to gain insight into customers, markets and organizations. Therefore, processing these data for obtaining decisions is of great challenge. Based on decision theory, in the past many methods are introduced to solve multi criterion decision making problem. The limitation of these approaches is that, they consider only certain information of the weights and decision values to make decisions. Alternatively, it makes less useful when managing uncertain and vague information. In addition, an information system establishes relation between two universal sets. In such situations, multi criterion decision making is very challenging. Therefore, an effort has been made in this paper to process inconsistencies in data with the introduction of intuitionistic fuzzy rough set theory on two universal sets.

In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks that are investigated in two aspects. In the first aspect, feature space is divided into several local regions and each region is assigned with a highly competent classifier and in the second, the base classifiers are applied in parallel and equally experienced in some ways to achieve a group consensus. In this paper combination of two methods are used. An important consideration in classifier combination is that much better results can be achieved if diverse classifiers, rather than similar classifiers, are combined. To achieve diversity in classifiers output, the symmetric pairwise weighted feature space is used and the outputs of trained classifiers over the weighted feature space are combined to inference final result. In this paper MLP classifiers are used as the base classifiers. The Experimental results show that the applied method is promising.

Shuffled Frog Leap Algorithm (SFLA), a metaheuristic algorithms inspired by PSO and DE has proved its efficacy in solving discrete optimization problems. In this paper we have modified SFLA to solve constrained engineering design problems. The proposed modification integrates a simple mechanism to update the position of frog in its memeplex in order to accelerate the basic SFLA algorithm. The proposal is validated on four engineering design problems and the statistical results are compared with the state-of-art algorithms. The simulated statistical results indicate that our proposal is a promising alternative to solve these types of optimization problems in terms of convergence speed.

The current tendency in the petrochemical plants and Aerospace industry is to save weight and life of the equipments in the processing units, in order to reduce costs and to ensure remaining life of components. The world of today is improving with respect to new technologies. As a result, new composites are added to material for gaining a new shape and structure of material. This has made it compulsory for new innovative in non destructive testing specially in ultrasonic inspection, from conventional ultrasonic based on pulse echo method to phased array methods. In parallel to this innovative in ultrasonic, mechanical manipulator have evolved for grinding and take a setup to unapproachable zones. Industrial robot with ultrasonic inspection developed in this paper, with the collaboration of Indian Oil Corporation, Faridabad, India. Which are bringing reliability, durability, accuracy, flexibility, good maintenance and reproducibility for different sizing components to in-service inspection? This paper focused on the evolution of in-service ultrasonic probe with mechanical manipulator in non destructive testing. This evolution system has led to the current scenario systems, where a perfect combination of innovative methods in ultrasonic techniques and robots is meet to our expected delivers.

Hybrid algorithm based on Particle Swarm Optimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.