Multi-objective whale optimization algorithm and multi-objective grey wolf optimizer for solving next release problem with developing fairness and uncertainty quality indicators

2021 ◽  
Author(s):  
Mohsen Ghasemi ◽  
Karamollah Bagherifard ◽  
Hamid Parvin ◽  
Samad Nejatian ◽  
Kim-Hung Pho
Keyword(s):  
Quality Indicators ◽  
Optimization Algorithm ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Multi Objective ◽  
Whale Optimization ◽  
Next Release Problem

Dynamic characterization of a master–slave robotic manipulator using a hybrid grey wolf–whale optimization algorithm

2021 ◽  
pp. 107754632110034
Author(s):  
Ololade O Obadina ◽  
Mohamed A Thaha ◽  
Kaspar Althoefer ◽  
Mohammad H Shaheed
Keyword(s):  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Robot Manipulator ◽  
Moving Average ◽  
Identification Problem ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Parametric Modelling ◽  
Grey Wolf ◽  
Whale Optimization

This article presents a novel hybrid algorithm based on the grey-wolf optimizer and whale optimization algorithm, referred here as grey-wolf optimizer–whale optimization algorithm, for the dynamic parametric modelling of a four degree-of-freedom master–slave robot manipulator system. The first part of this work consists of testing the feasibility of the grey-wolf optimizer–whale optimization algorithm by comparing its performance with a grey-wolf optimizer, whale optimization algorithm and particle swarm optimization using 10 benchmark functions. The grey-wolf optimizer–whale optimization algorithm is then used for the model identification of an experimental master–slave robot manipulator system using the autoregressive moving average with exogenous inputs model structure. Obtained results demonstrate that the hybrid algorithm is effective and can be a suitable substitute to solve the parameter identification problem of robot models.

scholarly journals Spatial Prediction of Landslide Susceptibility Using GIS-Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)

2019 ◽  
Vol 9 (18) ◽  
pp. 3755 ◽  
Author(s):  
Wei Chen ◽  
Haoyuan Hong ◽  
Mahdi Panahi ◽  
Himan Shahabi ◽  
Yi Wang ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Landslide Susceptibility ◽  
Heuristic Algorithms ◽  
Whale Optimization Algorithm ◽  
Economic Losses ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Prediction Ability ◽  
Inference System ◽  
Whale Optimization

The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro-fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta-heuristic algorithms to improve the prediction performance of the ANFIS-based methods. In addition, the step-wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root-mean-square error (RMSE). The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA-ANFIS-GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-GWO, and SWARA-ANFIS-WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

Enhanced whale optimization algorithm for sizing of hybrid wind/photovoltaic/diesel with battery storage in Algeria desert

2021 ◽  
pp. 0309524X2110565
Author(s):  
Adel Yahiaoui ◽  
Abdelhalim Tlemçani
Keyword(s):  
Renewable Energy ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Energy System ◽  
Whale Optimization Algorithm ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Hybrid Renewable Energy System ◽  
Whale Optimization ◽  
Hybrid Renewable Energy

This paper focuses on the optimization and operation of the renewable energy power sources for electrification of isolated rural city in Algeria desert. For this purpose, a system composed by photovoltaic (PV), wind turbine (WT), diesel generator (DG), and battery bank (BB) as well as for storing the energy in the electrical form to meet the load. In the present paper we are interested in evolutionary algorithms for solving optimization problem of hybrid renewable energy system. A new meta-heuristic algorithm namely whale optimization algorithm (WOA) is used to solve optimization problem of cost of energy (COE) and total net present cost (TNPC) including reliability evaluation by using basic probabilistic concept in order to find Loss of Power Supply Probability (LPSP). The WOA mimics the social behavior of humpback whales. This algorithm is inspired by the bubble-net hunting strategy. Three recent algorithms, particle swarm optimization (PSO), grey wolf optimizer (GWO), and modified grey wolf optimizer (M-GWO) are also implemented in this work. For examining the accuracy, stability, and robustness of proposed optimization technique two case studies have been tested. The results of simulations and comparison with other methods exhibit high accuracy and validity of the proposed whale optimization algorithm to solve optimization problem of hybrid renewable energy system.

scholarly journals Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2628
Author(s):  
Mengxing Huang ◽  
Qianhao Zhai ◽  
Yinjie Chen ◽  
Siling Feng ◽  
Feng Shu
Keyword(s):  
Optimization Algorithm ◽  
Edge Computing ◽  
Solution Set ◽  
Whale Optimization Algorithm ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Multi Objective ◽  
Whale Optimization ◽  
Artificial Immune System Algorithm

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

A Cloud-based Conceptual Framework for Multi-Objective Virtual Machine Scheduling using Whale Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Nadim Rana ◽  
Muhammad Shafie Abd Latiff ◽  
Shafi'i Muhammad Abdulhamid
Keyword(s):  
Conceptual Framework ◽  
Virtual Machine ◽  
Optimization Algorithm ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Machine Scheduling ◽  
Whale Optimization Algorithm ◽  
Multi Objective ◽  
Whale Optimization ◽  
Virtual Machine Scheduling

Virtual machine scheduling in the cloud is considered one of the major issue to solve optimal resource allocation problem on the heterogeneous datacenters. With respect to that, the key concern is to map the virtual machines (VMs) with physical machines (PMs) in a way that maximum resource utilization can be achieved with minimum cost. Due to the fact that scheduling is an NP-hard problem, a metaheuristic approach is proven to achieve a better optimal solution to solve this problem. In a rapid changing heterogeneous environment, where millions of resources can be allocated and deallocate in a fraction of the time, modern metaheuristic algorithms perform well due to its immense power to solve the multidimensional problem with fast convergence speed. This paper presents a conceptual framework for solving multi-objective VM scheduling problem using novel metaheuristic Whale optimization algorithm (WOA). Further, we present the problem formulation for the framework to achieve multi-objective functions.

Sign in / Sign up

Export Citation Format

Share Document