Gaurav Dhiman

Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications

Abstract This paper presents a novel metaheuristic algorithm named as Spotted Hyena Optimizer (SHO) inspired by the behavior of spotted hyenas. The main concept behind this algorithm is the social relationship between spotted hyenas and their collaborative behavior. The three basic steps of SHO are searching for prey, encircling, and attacking prey and all three are mathematically modeled and implemented. The proposed algorithm is compared with eight recently developed metaheuristic algorithms on 29 well-known benchmark test functions. The convergence and computational complexity is also analyzed. The proposed algorithm is applied to five real-life constraint and one unconstrained engineering design problems to demonstrate their applicability. The experimental results reveal that the proposed algorithm performs better than the other competitive metaheuristic algorithms.

Multi-objective spotted hyena optimizer: A Multi-objective optimization algorithm for engineering problems

Abstract This paper proposes a multi-objective version of recently developed Spotted Hyena Optimizer (SHO) called Multi-objective Spotted Hyena Optimizer (MOSHO). It is used to optimize the multiple objectives problems. In the proposed algorithm, a fixed-sized archive is employed for storing the non-dominated Pareto optimal solutions. The roulette wheel mechanism is used to select the effective solutions from archive to simulate the social and hunting behaviors of spotted hyenas. The proposed algorithm is tested on 24 benchmark test functions and compared with six recently developed metaheuristic algorithms. The proposed algorithm is then applied on six constrained engineering design problems to demonstrate its applicability on real-life problems. The experimental results reveal that the proposed algorithm performs better than the others and produces the Pareto optimal solutions with high convergence.

Emperor penguin optimizer: A bio-inspired algorithm for engineering problems

Abstract This paper proposes a novel optimization algorithm, called Emperor Penguin Optimizer (EPO), which mimics the huddling behavior of emperor penguins (Aptenodytes forsteri). The main steps of EPO are to generate the huddle boundary, compute temperature around the huddle, calculate the distance, and find the effective mover. These steps are mathematically modeled and implemented on 44 well-known benchmark test functions. It is compared with eight state-of-the-art optimization algorithms. The paper also considers for solving six real-life constrained and one unconstrained engineering design problems. The convergence and computational complexity are also analyzed to ensure the applicability of proposed algorithm. The experimental results show that the proposed algorithm is able to provide better results as compared to the other well-known metaheuristic algorithms.

A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches

Abstract In this article, a novel M-factors fuzzy time series (FTS) forecasting model is presented, which relies upon on the hybridization of two procedures, viz., granular computing and bio-inspired computing. In this investigation, granular computing is utilized to discretize M-factors time series data set to obtain granular intervals. These intervals are additionally used to fuzzify the time series data set. Based on fuzzified time series data set, M-factors fuzzy relations are set-up. These M-factors fuzzy relations are further utilized to acquire forecasting results. Moreover, a novel bio-inspired algorithm is proposed to enhance the forecasting accuracy. The main objective of this algorithm is to adjust the lengths of the intervals (granular and non-granular intervals) in the universe of discourse that are used in forecasting. The proposed model is verified and validated with various real world data sets. Various statistical and comparative analyzes signify that the proposed model can take far better decision with the M-factors time series data sets. Moreover, empirical analysis demonstrates that forecasting accuracy of the proposed model based on granular intervals is better than non-granular intervals.

An Analysis of Modeling and Optimization Production Cost Through Fuzzy Linear Programming Problem with Symmetric and Right Angle Triangular Fuzzy Number

The main objective of this paper is to do the modeling and optimization of production cost of RCF kapurthala using TFLPP-(s, l, r) and triangular (Right angle) fuzzy linear programming problem. The total costs of the different constrains are vacillating or uncertain, so to minimize the production cost, fuzzy LPP (right angle triangular) and TFPP- (s, l, r) model are used. Owing to probabilistic increments in the availability of different constrains, the actual cost of production is to leading the destruction. Here the situational based Fuzzy model is being expressed to mitigate the destruction in the cost optimization and examining the credibility of optimized value. The data of RCF Kapurthala constitutes the production cost of different coaches from the year 2009–10. The total cost has been targeted to optimize with respect to the constraints of Labor cost, Material cost, Administrative overhead charges, Factory overhead charges, Township overhead charges, Shop overhead charges and Performa charges. The lower and upper bound have been calculated using TFLPP-(s, l, r), TFLPP-(s, l), TFLPP-(s, r) and TFLPP-(s) for the objective function of the optimized fuzzy LPP. This optimized fuzzy LPP will provide the membership grade for the optimized production cost.

Spotted Hyena Optimizer for Solving Complex and Non-linear Constrained Engineering Problems

This paper presents a metaheuristic optimization algorithm named as Spotted Hyena Optimizer (SHO) for solving complex and nonlinear constrained engineering problems. The fundamental concept of this algorithm is the hunting strategy of spotted hyena in nature. The three basic steps of proposed SHO algorithm are searching, encircling, and attacking for prey. The proposed SHO algorithm is applied to two real-life complex and nonlinear constrained engineering problems to ensure its applicability in high-dimensional environment. The experimental results of engineering problems reveal that SHO algorithm outperforms other competitive approaches.

Uncertainty representation using fuzzy-entropy approach: Special application in remotely sensed high-resolution satellite images (RSHRSIs)

Abstract Remotely sensed high-resolution satellite images contain various information in context of changes. By analyzing this information very minutely, changes occurred in various atmospheric phenomena can be identified. Therefore, in this study, a novel change detection method is proposed using the fuzzy set theory. The proposed method represents the uncertain changes in the form of a fuzzy set using the corresponding degree of membership values. By using the fuzzy set operators, such as max and min functions, this study derives very useful information from the images. This study also proposes a new function to identify the boundary of uncertain changes. Further, this study is propagated to identify the similarity or dissimilarity between different images of the same event that contain various uncertain changes. To recognize the changes in a fine-grained level, this study introduces a way to represent the fuzzy information in a granular way. The utilization of the proposed method is shown by recognizing changes and retrieving information from the remotely sensed high-resolution satellite images. Various experimental results exhibit the robustness of the study.

A Review on Search-Based Tools and Techniques to Identify Bad Code Smells in Object-Oriented Systems

Researchers have provided various techniques and tools in the past few years for identification of code smells. Due to their changing outcomes and features, the classification, comparison, and evaluation of this existing code smell detection techniques and tool are imperative. This paper presents the current state of the art in the area of approaches that use search-based techniques to identify code smell from the source code of object-oriented systems. The classification of code bad smells approaches is done on the basis of their detection and analysis method. The results of selected techniques were analyzed. The observations and recommendations were presented after critical analysis of existing code smell detection approaches. These observations and recommendations can help the researchers and practitioners working in the area of designing a tool/technique for code smell detection.

A four-way decision-making system for the Indian summer monsoon rainfall

Due to non-stationary nature of Indian summer monsoon rainfall (ISMR), analysis of its patterns and behaviors is a very tedious task. Advance prediction and behaviors play a significant role in var...

A Fuzzy-LP Approach in Time Series Forecasting

In this study, a novel model is presented to forecast the time series data set based on the fuzzy time series (FTS) concept. To remove various drawbacks associated with the FTS modeling approach, this study incorporates significant changes in the existing FTS models. These changes are: (a) to apply the linear programming (LP) model in the FTS modeling approach for the selection of appropriate length of intervals, (b) to fuzzify the historical time series value (TSV) based on its involvement in the universe of discourse, (c) to use the high-order fuzzy logical relations (FLRs) in the decision making, and (d) to use the degree of membership (DM) along with the corresponding mid-value of the interval in the defuzzification operation. All these implications signify the effective results in time series forecasting, which are verified and validated with real-world time series data set.