Application of Whale Optimization Algorithm to Inverse Scattering of an Imperfect Conductor with Corners

Through the research and analysis of a relatively novel natural heuristic, meta-heuristic swarm intelligence optimization algorithm, this swarm intelligence algorithm is defined as a whale optimization algorithm. The algorithm builds a mathematical model by simulating a social behavior of humpback whales. This optimization algorithm was inspired by the bubble-like net hunting phenomenon that humpback whales prey on. By analyzing the four benchmark optimization problems with or without offset and rotation, the convergence performance of the whale optimization algorithm and the ability to solve the optimization problem are proved. The performance of the whale optimization algorithm is based on the computer simulation technology. Through the convergence curve obtained from the experiment, we can see that the whale optimization algorithm performs best for the five benchmark optimization problems without rotation.

Irrelevant features in data collected from oil refineries affect system performance due to conflicts between normal data and detected faults. Selecting the relevant features from the data leads to better classification results. Optimization algorithms are successfully applied in the feature selection task in many systems. One of the powerful optimization algorithms that is used for feature selection is the Whale Optimization Algorithm (WOA), which is a nature-inspired metaheuristic optimization algorithm that mimics the social behavior of humpback whales. This study presents an improvement to WOA using a tent chaotic map to select the most relevant features and enhance performance. The Tent map mainly applies randomness to generate diversification into the search process and escape from local optima in WOA. The tent map is used for generating the initial population, producing values in control parameters, and updating the position of search agents. The proposed approach combines the tent map with WOA, called TWOA, to enrich population diversity, prevent premature convergence, and speed up convergence. TWOA is applied in a soft sensor with actual data collected from the Salahuddin oil refinery in Iraq. The soft sensor was designed using several stages, including data collection, preprocessing, clustering, feature selection, and classification. The proposed TWOA achieved a higher fault classification result of 99.98% compared to other algorithms.

Snake Optimizer: A novel meta-heuristic optimization algorithm

In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy of the classification while preserving the minimum number of features. The proposed model is compared with the particle swarm optimization (PSO) and genetic algorithm (GA) using a number of assessment indicators on 16 different data-sets from UCI data repository. The results demonstrate the advantage of the introduced algorithm compared to the other optimizers.

The inherent variability and uncertainty of solar radiation, influenced by factors such as seasons, weather, and cloud cover, pose significant challenges in accurately forecasting energy output from solar power systems. Robust forecasting models that can capture the dynamic behavior of solar radiation are crucial for optimizing energy generation, distribution, and storage processes. This study proposes an integrated approach that combines neural networks, metaheuristic optimization algorithms, and principal component analysis (PCA) for precise solar radiation forecasting. The methodology uses one year of solar radiation data from November 2022 to October 2023 from Vellore district, India. PCA is employed for dimensionality reduction, mitigating the curse of dimensionality. The reduced dataset is segregated into winter, summer, and monsoon subsets to capture distinct seasonal patterns. Neural networks are trained on these seasonal datasets, with their weights optimized by metaheuristic algorithms, including particle swarm optimization (PSO), gray wolf optimization (GWO), whale optimization algorithm (WOA), and coati optimization algorithm (COA). A comprehensive comparative study evaluated the performance of ANN-PSO, ANFIS-GWO, ANN-WOA, ANN-COA, and traditional ANN models. The ANN-COA model, integrating ANN with COA for weight optimization and PCA for dimensionality reduction, achieved the most accurate forecasts, outperforming other approaches. It attained optimal RMSE of 12.06 for winter, 6.72 for summer, and 9.49 for monsoon season, MAE of 6.66, 5.50, and 6.25, MAPE of 3.58 %, 1.68 %, and 2.31 %, and R2 of 0.973, 0.982, and 0.985 for winter, summer, and monsoon seasons respectively, demonstrating its robustness and suitability for time-series solar radiation forecasting.

With the advance of the communication technology and the massive flow of information across the internet, it is becoming urgent to keep the confidentiality of the transmitted information. Using the internet has been extended to several fields such as e-mail, e-commerce, e-learning, health and medicine, shopping, and so on. Cryptography is the study of different techniques for securing the communication between the sender and the receiver. One of the most known cryptosystems is Merkle–Hellman Knapsack Cryptosystem (MHKC). It is one of the earliest Public Key Cryptosystem (PKC) that is used to secure the messages between the sender and the receiver. Developing a powerful cryptosystem comes after studying the fragility points of the current cryptosystems. The Whale Optimization Algorithm (WOA) is one of the most recent nature-inspired meta-heuristic optimization algorithms, which simulates the social behavior of humpback whales. WOA has validated excellent performance in solving the continuous problems and the engineering optimization problems. This paper introduces a novel Modified version of WOA (MWOA) for cryptanalysis of MHKC. The sigmoid function is used to map the continuous values into discrete one. A penalty function is added to the evaluation function to deal with the infeasible solutions. The mutation operation is employed for improving the solutions. The results show that MWOA is more effective and robust than other algorithms in the literature.

Whale Optimization Algorithm (WOA) proposed by Seyedali Mirjalili and Andrew Lewis in 2016 is popular and powerful metaheuristic algorithm to search the global solution of optimization problems. WOA is nature-inspired, metaheuristic (randomization and deterministic) algorithm, which has been widely used to solve various single objective, multi objective and multidimensional optimization problems. WOA and its variant have been introduced in engineering applications, bioinformatics, multi-level image segmentation, clustering applications, design of low pass filter, Email classification, Diabetes classification, heterogeneous networks, machine learning etc. WOA is gradient free, easy to represent, capable to explore, exploit the search space and able to avoid local optima. This paper presents overview of WOA, its variants and applications. The performance of WOA is enhanced by introducing hybridization of other methods with WOA such as WOA-PSO, WOA-Levy, WOA-BAT, WOA-ANN, WOA-SVM etc. Objective of metaheuristic algorithm is tofind best position or leader position X* which is near to optimal solution for target prey over successive iteration. Objective function could be based on minimization or maximization approach.

PurposeSea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.Design/methodology/approachCloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.FindingsThe performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.Originality/valueTo the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.

Microgrid becomes an attractive concept to meet the rapidly increasing demands for energy in worldwide and deal with air pollutions. Distributed energy resources (DERs) in Microgrid are the vital role to meet the demand of the grid locally. The performance and the PV source grid connected system's response depend on vital parameters like transient response, voltage or current overshoot, steady state error, and harmonics distortions. This research aims to optimize the proportional-integral (PI) controller for improving power quality performance and energy management of the PV battery of three phase inverter of grid connected mode scheme. Besides to the PI conventional method, three different meta-heuristic optimization algorithms are proposed and implemented in order to enhance the PI and hence all system parameters. These three algorithms are (Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Dragonfly Algorithm (DA)) that can tune the PI controller's parameters by minimizing voltage regulator error, hence approving the goal of optimization in finding the optimal local or global solutions. The experimental results have been demonstrating the variety and the effectiveness of these optimization methods in convergence generation, computation time, and improving all required system parameters.

Abstract. This paper focuses on the 4-PUU parallel mechanism with Schönflies motion, investigating its kinematic analysis and utilizing intelligent algorithms to solve its forward kinematics equations. Using screw theory as the mathematical framework, the number and nature of the degrees of freedom of the 4-PUU parallel mechanism are analyzed, proving that the mechanism can achieve 3T1R motion. The forward and inverse position kinematics equations are established with link lengths as constraints, and the velocity and acceleration equations of the mechanism are derived using the vector method. The forward kinematics equations of the mechanism are transformed into an unconstrained optimization problem, which is solved using an improved beluga whale optimization (BWO). To enhance the uniformity of the initial population distribution, a chaotic-opposition-based learning initialization strategy is introduced. Additionally, an elite strategy and a golden-sine-based position update mechanism are incorporated to improve the optimization capability of BWO. The integration of these strategies results in an enhanced optimization algorithm with superior global search ability. Given the mechanism's dimensional parameters, theoretical and motion simulations are conducted using MATLAB and Adams. The results indicate that the motion simulation curves from Adams closely match the numerical simulation curves from MATLAB, validating the theoretical derivation. Building on this, the improved beluga whale optimization (IBWO) algorithm, along with comparison algorithms (DEb1, ABC, PSO), is applied to solve the forward kinematics equations for interpolation points along the simulated trajectory. Numerical experiments demonstrate that IBWO outperforms the other comparison algorithms in solving the problem efficiently and accurately.

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

In this article, a new hybrid metaheuristic optimization algorithm is proposed to solve the coordination problem of directional overcurrent relays (DOCRs). The proposed algorithm is constructed using hybrid whale optimization algorithm and gray wolf optimizer (HWGO) that enhance the performance and reliability of the traditional whale optimization algorithm (WOA). The proposed method enhances the exploitative phase of the WOA using a leadership hierarchy of the gray wolf optimizer (GWO) to find the best optimum solution. The coordination problem of DOCRs is subject to numerous constraints. The goal function for optimal coordination of DOCRs aims to minimize total operation time for all primary relay without violation in constraints to maintain reliability and security of the electric power system. The effectiveness of the proposed algorithm has been investigated on four different interconnected networks. The results using HWGO algorithm are compared with the original WOA, GWO, and earlier reported results of other optimization techniques. The results prove the viability of the proposed algorithm to solve the DOCR coordination problem and the ability of the proposed algorithm to overcomes the drawbacks and cover the weakness of the conventional WOA.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

Meta-heuristic optimization algorithms for design of gain constrained state variable filter

Henry gas solubility optimization: A novel physics-based algorithm