A novel quasi-oppositional chaotic antlion optimizer for global optimization

Saha, Subhodip; Mukherjee, V.

doi:10.1007/s10489-017-1097-7

Cited by 39 publications

(22 citation statements)

References 71 publications

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“…Saha and Mukherjee in [136] introduced a new version of ALO called (QOCALO) using quasi-oppostional chaotic method. In their proposed algorithm, the initialization of the first population is done using quasi-oppositional based learning (QOBL).…”

Section: ) Chaotic Alomentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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Ant Lion Optimizer (ALO) is a recent novel algorithm developed in the literature that simulates the foraging behavior of a Ant lions. Recently, it has been applied to a huge number of optimization problems. It has many advantages: easy, scalable, flexible, and have a great balance between exploration and exploitation. In this comprehensive study, many publications using ALO have been collected and summarized. Firstly, we introduce an introduction about ALO. Secondly, we categorized the recent versions of ALO into 3 Categories mainly Modified, Hybrid and Multi-Objective. we also introduce the applications in which ALO has been applied such as power, Machine Learning, Image processing problems, Civil Engineering, Medical, etc. The review paper is ended by giving a conclusion of the main ALO foundations and providing some suggestions & possible future directions that can be investigated.

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Section: ) Chaotic Alomentioning

confidence: 99%

Ant Lion Optimization: Variants, Hybrids, and Applications

2020

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“…In [9] authors improved the GWO algorithm by adding the concept of random walk and evaluated the performance of the algorithms on various benchmark functions. Quasi-opposition based strategy was incorporated in ant-lion optimization in [10] along with chaotic maps. In [11] authors proposed the use of Grasshopper Optimization with Evolutionary Population Dynamics based strategy for feature selection.…”

Section: Related Workmentioning

confidence: 99%

Opposition-Based Enhanced Grey Wolf Optimization Algorithm for Feature Selection in Breast Density Classification

Hans¹,

Kaur²

2020

IJMLC

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Breast Cancer is the most widespread cancer amongst women in the world. An increase in the density of breast may lead to an increase in the risk of breast cancer. For the accurate diagnosis of breast cancer, these days Machine learning-based computer-aided diagnosis systems are widely used to assist the radiologists. However, there is still a scope of improvement in the computer-aided diagnosis system for the feature selection problem, which is considered as an optimization problem. Optimization techniques are the methods where the finest solution to a problem is found by using random search mechanism. Grey wolf optimization is one of the most recent optimization algorithms. This paper presents an enhanced version of Grey Wolf Optimization algorithm and tests the performance of the proposed version of the algorithm on thirteen different benchmark functions and the binary version of the proposed algorithm is used for selecting optimal number of features for breast density classification problem.

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“…The effective tuning of these controllers in ALFC of these complex microgrids is achieved by applying some basic optimization algorithms such as: particle swarm optimization (PSO) [18], grasshopper optimization algorithm (GOA) [20,21], salp swarm algorithm (SSA) [25], selfish-herd optimization (SHO) [26], or their hybrids such as quasi-oppositional selfish-herd optimization (QSHO) [1,17]. Likewise, the quasi-oppositional chaotic antlion optimizer algorithm is projected in [27] by hybridizing quasi-opposition-based learning (QOBL) and chaotic linear search (CLS) techniques with antlion optimization. Inspiringly, a novel algorithm named "quasi-oppositional chaotic selfish-herd optimization" (QCSHO) is proposed here for tuning the PID controllers by hybridizing QOBL and CLS techniques [27] with the basic SHO [28] algorithm to replicate the chaotic behavior of the selfish herds.…”

Section: Introductionmentioning

confidence: 99%

Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

et al. 2021

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This work is the earliest attempt to propose an integrated resource planning for distributed hybrid microgrids considering virtual-inertia support (VIS) and demand-response support (DRS) systems. Initially, three-distributed sustainable energy-based unequal hybrid microgrids are envisioned with the availability of solar/wind/bioenergy resources. In order to overcome the effects of intermittency in renewable resources and low inertia, each microgrid is incorporated with DRS and VIS units for demand- and supply-side management, respectively. The proposed system is simulated in MATLAB considering real-time recorded solar/wind data with realistic loading for 12 months. A novel quasi-oppositional chaotic selfish-herd optimization (QCSHO) algorithm is proposed by hybridizing quasi-opposition-based learning and chaotic linear search techniques into the selfish-herd optimization, for optimal regulation of voltage and frequency in microgrids. Then, the system responses are compared with 7 algorithms and 5 error functions to tune PID controllers’ gains, which confirmed the superiority of QCSHO over others. Then, the study proceeds to investigate the voltage, frequency, and tie-line power coordination in 5 extreme scenarios of source and load variations in the proposed system without retuning the controllers. Finally, the system responses are analyzed for 10 different possible allocation of VIS and DRS units in different microgrids to find the most suitable combinations, and the results are recorded.

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A novel quasi-oppositional chaotic antlion optimizer for global optimization

Cited by 39 publications

References 71 publications

Ant Lion Optimization: Variants, Hybrids, and Applications

Ant Lion Optimization: Variants, Hybrids, and Applications

Opposition-Based Enhanced Grey Wolf Optimization Algorithm for Feature Selection in Breast Density Classification

Optimal Voltage–Frequency Regulation in Distributed Sustainable Energy-Based Hybrid Microgrids with Integrated Resource Planning

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