Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization

Jain, Mamta; Maurya, Shubham; Rani, Asha; Singh, Vijander

doi:10.3233/jifs-169452

Cited by 112 publications

(44 citation statements)

References 21 publications

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“…The exponential Monte Carlo with counter (EMCQ) algorithm from [3,5,6] has been adopted in this work as the basis of Q-EMCQ selection and acceptance mechanism. EMCQ algorithm accepts poor solution (similar to simulated annealing [7]) to escape from local optima.…”

Section: Q-learning Monte Carlo Hyper-heuristic Strategymentioning

confidence: 99%

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications

et al. 2020

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Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the Exponential Monte Carlo with Counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t − wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results of this study show that Q-EMCQ is an efficient technique for test case generation. Additionally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including Modified Choice Function (MCF), Tabu High Level Hyper-Heuristic(HHH), Teaching Learning based Optimization (TLBO), Sine Cosine Algorithm (SCA) and Symbiotic Optimization Search (SOS) in clustering quality

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Section: Q-learning Monte Carlo Hyper-heuristic Strategymentioning

confidence: 99%

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications

et al. 2020

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“…So by balancing these two strategies, on the one hand, the search leads to areas of better answering space, and on the other hand, wastes no more time in the part of the solution space that previously reviewed or included inferior solutions. In 2018, Jain et al 26 introduced a new meta‐heuristic technique called the owl search algorithm (OSA). The OSA was inspired by the promising behavior of the hunting characteristics of the owls.…”

Section: Barn Owl and Inspiration For Optimizationmentioning

confidence: 99%

“…The prey can be hidden at night by hearing sense instead of vision sense 38 . The sound which is generated by a prey can be processed by two parts of the owl's brain: the interaural time difference (ITD) and the interaural loudness difference (ILD) which are employed for preparing the auditory map of the prey location 26 . The prey distance can be estimated by the owl based on intensity and time differences of sound wave arrival.…”

Section: Barn Owl and Inspiration For Optimizationmentioning

confidence: 99%

“…The term meta‐heuristic was first developed by Glover in 1993 when he introduced the Tabu search method as a new innovative method 17 . There are several types of meta‐heuristics such as quantum invasive weed optimization (QIWO), 18 pigeon‐inspired optimization algorithm, 19 world cup optimization (WCO), 12,20‐22 socio evolution and learning optimization (SELO), 23 states of matter search (SMS), 24 rhino herd behavior (RHB), 25 and Owl search algorithm 26 . As a result, they can be used to optimize the size and size of the CCHP systems.…”

Section: Introductionmentioning

confidence: 99%

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Improved owl search algorithm for optimal capacity determination of the gas engine in aCCHPsystem using4Eanalysis

Cao

Kui

Ashourian

2020

Int Trans Electr Energ Syst

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“…In [31], a power efficient scheduling and Virtual Machine (VM) Consolidation algorithm is proposed for effectively managing energy resources. In [32], a population-based nature-inspired optimization algorithm that simulates the hunting mechanism of barn owls is proposed. In [33], the authors present an extended version of Teaching Learning Based Optimization (TLBO) for enhancing the exploration and exploitation capacities by introducing the concept of Neighbour Learning strategy.…”

mentioning

confidence: 99%

Soft computing and intelligent systems: Techniques and applications

Thampi

El-Alfy

Trajković

2018

IFS

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This special issue is a collection of selected papers submitted to the Third International Symposium on Intelligent Systems Technologies and Applications (ISTA' 17) held in Manipal University, India, during September 13-16, 2017. These papers have been peer reviewed and accepted for presentation at the symposium. A second review has been conducted to improve content and presentation of the manuscripts published in this special issue. The 48 papers cover a wide range of powerful techniques and current applications of soft computing and intelligent systems.The International Symposium on Intelligent Systems Technologies and Applications aims to bring together researchers in related fields and provides a venue to explore and discuss various aspects of intelligent systems technologies and their applications. It provides excellent opportunities for the presentation and discussion of interesting new research results, which contributes to effective transfer of knowledge and dissemination of innovative ideas.Listed is a summary of the contributions of the papers pertaining to various application domains:Cybersecurity is an important concern for every organization. In this special issue, twelve papers * Corresponding author. Sabu M. Thampi, Indian Institute of Information Technology and Management-Kerala, Technopark Campus, Trivandrum-695581, Kerala State, India. E-mail: sabu.thampi@iiitmk.ac.in. deal with intrusion detection, integrity, and confidentiality. In [1], the authors describe an intrusion detection system for wireless mesh networks that identifies intrusion activities using a support vector machine classifier. The proposed method employs a hybrid genetic algorithm and a mutual-information technique for feature selection. In [2], a scheme is proposed to provide a secure outsourcing solution for the matrix-chain multiplication problem in cloud computing. For preserving data confidentiality, the client employs few linear transformation schemes. The proposed verification scheme helps to maintain the integrity of the computed result. In [3], the authors evaluate the efficiency of various deep learning methods to detect and classify domain names as either malicious or benign by automatically extracting the required features thus reducing the burden of manual feature engineering approaches. In [4], the effectiveness of recurrent neural network (RNN) and its variant long short-term memory (LSTM) is analyzed for Android malware detection of time-varying sequences of benign and malicious applications. This paper also provides a comparative analysis with other machine-learning classifiers. In [5], the authors propose a data-driven intrusion detection system for Industrial Internet of Things (IIoT) to hybridize both anomaly and specification-based schemes. In [6], an intelligent monitoring technique for smart grid

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Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization

Cited by 112 publications

References 21 publications

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications

An evaluation of Monte Carlo-based hyper-heuristic for interaction testing of industrial embedded software applications

Improved owl search algorithm for optimal capacity determination of the gas engine in aCCHPsystem using4Eanalysis

Soft computing and intelligent systems: Techniques and applications

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