Application of Whale Optimization Algorithm for tuning of a PID controller for a drilling machine

International audienceThe aim of this work is to implement the recently developed metaheuristic algorithm known as the Whale Optimization Algorithm to tune a PID controller of a high-performance drilling machine. The algorithm is evaluated by setting the Integral Absolute Error as the objective function. The simulation results are then compared with the widely used conventional tuning technique namely Ziegler-Nichols (Z-N) along with another commonly used evolutionary computation technique, the Particle Swarm Optimization (PSO). The results obtained in this work indicates that this novel algorithm can give satisfactory results while tuning the PID controller. Index Terms-Meta-heuristic algorithm, Whale optimization algorithm, PID controller

Hybrid Encryption based Access Control Approach for Securing Cloud Computing

                Cloud computing has become an integral part of modern technological infrastructure, facilitating the storage and processing of vast amounts of data. However, ensuring the security of sensitive information in the cloud remains a persistent challenge. This paper proposes a novel approach to enhance the security of cloud computing through hybrid encryption, leveraging the Whale Optimization Algorithm (WOA) and Cuckoo Search Optimization (CSO) algorithms. Hybrid encryption, combining symmetric and asymmetric cryptographic techniques, is employed to address the limitations of traditional encryption methods in cloud environments. The Whale Optimization Algorithm and Cuckoo Search Optimization are utilized to optimize key generation and management processes, enhancing the overall efficiency and security of the encryption scheme. The Whale Optimization Algorithm, inspired by the social behavior of humpback whales, is employed to optimize the parameters of the encryption algorithm. WOA's exploration and exploitation capabilities are leveraged to find an optimal balance in the encryption process, improving the overall robustness against potential attacks. Complementing WOA, the Cuckoo Search Optimization algorithm is applied to optimize the key distribution and update mechanisms. Modeled after the brood parasitism behavior of cuckoo birds, CSO excels in searching large solution spaces, making it suitable for refining the distribution of encryption keys and ensuring their constant adaptability to dynamic cloud environments

Dynamic Regulation of Fisheries: the Case of the Bowhead Whale

The regulation of fisheries often requires finding numerical solutions to dynamic optimization problems. This paper presents a version of the "multiple shooting" algorithm and uses it to approximate the dynamic solution to a fisheries problem examined by Conrad (1989): the hunting of the Bowhead whale in the Western Arctic. It is found that the inclusion of dynamic considerations can significantly alter the nature of the policy if the regulated population is not near its steady state.Bowhead whale, multiple shooting, numerical methods, regulation of fisheries., Environmental Economics and Policy,

Load Balancer using Whale-Earthworm Optimization for Efficient Resource Scheduling in the IoT-Fog-Cloud Framework

Cloud-Fog environment is useful in offering optimized services to customers in their daily routine tasks. With the exponential usage of IoT devices, a huge scale of data is generated. Different service providers use optimization scheduling approaches to optimally allocate the scarce resources in the Fog computing environment to meet job deadlines. This study introduces the Whale-EarthWorm Optimization method (WEOA), a powerful hybrid optimization method for improving resource management in the Cloud-Fog environment. Striking a balance between exploration and exploitation of these approaches is difficult, if only Earthworm or Whale optimization methods are used. Earthworm technique can result in inefficiency due to its investigations and additional overhead, whereas Whale algorithm, may leave scope for improvement in finding the optimal solutions using its exploitation.  This research introduces an efficient task allocation method as a novel load balancer. It leverages an enhanced exploration phase inspired by the Earthworm algorithm and an improved exploitation phase inspired by the Whale algorithm to manage the optimization process. It shows a notable performance enhancement, with a 6% reduction in response time, a 2% decrease in cost, and a 2% improvement in makespan over EEOA. Furthermore, when compared to other approaches like h-DEWOA, CSDEO, CSPSO, and BLEMO, the proposed method achieves remarkable results, with response time reductions of up to 82%, cost reductions of up to 75%, and makespan improvements of up to 80%

GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering Challenges and Beyond

This study proposes the GOOSE algorithm as a novel metaheuristic algorithm based on the goose's behavior during rest and foraging. The goose stands on one leg and keeps his balance to guard and protect other individuals in the flock. The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions, and the results are verified by a comparative study with genetic algorithm (GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10 modern benchmark functions, and the gained results are compared with three recent algorithms, such as the dragonfly algorithm, whale optimization algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm is tested on 5 classical benchmark functions, and the obtained results are evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX optimizer, butterfly optimization algorithm (BOA), whale optimization algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The achieved findings attest to the proposed algorithm's superior performance compared to the other algorithms that were utilized in the current study. The technique is then used to optimize Welded beam design and Economic Load Dispatch Problem, three renowned real-world engineering challenges, and the Pathological IgG Fraction in the Nervous System. The outcomes of the engineering case studies illustrate how well the suggested approach can optimize issues that arise in the real-world

A new health-based metaheuristic algorithm: cholesterol algorithm

This paper seeks to explore the effectiveness of a new health-based metaheuristic algorithm inspired by the cholesterol metabolism of the human body. In the study, the main idea is the focus on the performance of the cholesterol algorithm on unconstrained continuous optimization problems. The performances of the proposed cholesterol algorithm are evaluated based on 23 comparison tests and results were compared with Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Whale Optimization Algorithm, Harris Hawks Optimization, Differential Evolution, FireFly Algorithm, Cuckoo Search, Multi-Verse Optimizer, and JAYA algorithms. Results showed that this novel cholesterol algorithm implementation can compete effectively with the best-known solution to test functions

A WOA-based optimization approach for task scheduling in cloud Computing systems

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks

Research on Robot Path Perception and Optimization Technology based on Whale Optimization Algorithm

With the development of modern sensor technology, the automatic movement of robot has become a reality, and improving the path planning performance of robot in dynamic and complex environment is an important development direction of mobile robot intelligence. This time, based on the idea of hybrid path planning, whale optimization algorithm and computer perception technology are introduced to plan the path of the robot. In order to improve the performance of the traditional whale optimization algorithm, it is optimized and improved by genetic algorithm. Through the performance simulation analysis of the improved whale optimization algorithm, it can be seen that the improved algorithm has better path optimization performance and significantly improved efficiency compared with other traditional algorithms. It can perceive and calculate an optimized path