Grey wolf optimization approach for searching critical failure surface in soil slopes

Path planning for robots plays a vital role to seek the most feasible path due to power requirement, environmental factors and other limitations. The path planning for the autonomous robots is tedious task as the robot needs to locate a suitable path to move between the source and destination points with multifaceted nature. In this paper, we introduced a new technique named modified grey wolf optimization (MGWO) algorithm to solve the path planning problem for multi-robots. MGWO is modified version of conventional grey wolf optimization (GWO) that belongs to the category of metaheuristic algorithms. This has gained wide popularity for an optimization of different parameters in the discrete search space to solve various problems. The prime goal of the proposed methodology is to determine the optimal path while maintaining a sufficient distance from other objects and moving robots. In MGWO method, omega wolves are treated equally as those of delta wolves in exploration process that helps in escalating the convergence speed and minimizing the execution time. The simulation results show that MGWO gives satisfactory performance than other state of art methods for path planning of multiple mobile robots. The performance of the proposed method is compared with the standard evolutionary algorithms viz., Particle Swarm Optimization (PSO), Intelligent BAT Algorithm (IBA), Grey Wolf Optimization (GWO), and Variable Weight Grey Wolf Optimization (VW-GWO) and yielded better results than all of these.

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Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

10.26434/chemrxiv.11733393 ◽

2020 ◽

Author(s):

Kin Meng Wong ◽

Shirley Siu

Keyword(s):

Scoring Function ◽

Ligand Docking ◽

Receptor Protein ◽

Pose Prediction ◽

Grey Wolf ◽

Success Rates ◽

Autodock Vina ◽

Grey Wolf Optimization ◽

Structure Based Drug Design ◽

Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

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AN INTELLIGENT CRYPTO-LOCKER RANSOMWARE DETECTION TECHNIQUE USING SUPPORT VECTOR MACHINE CLASSIFICATION AND GREY WOLF OPTIMIZATION ALGORITHMS

i-manager’s Journal on Software Engineering ◽

10.26634/jse.13.3.15685 ◽

2019 ◽

Vol 13 (3) ◽

pp. 15

Author(s):

MOHAMMED MAIGIDA ABDULLAHI ◽

MUHAMMAD ABDULHAMID SHAFI'I ◽

OLALERE MORUFU ◽

ISMAILA IDRIS ◽

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...

Keyword(s):

Support Vector Machine ◽

Optimization Algorithms ◽

Detection Technique ◽

Support Vector ◽

Grey Wolf ◽

Grey Wolf Optimization ◽

Support Vector Machine Classification

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