Version Of Whale Optimization Algorithm Research Articles

Modern Temperature Control of Electric Furnace in Industrial Applications Based on Modified Optimization Technique

In this paper, an enhanced version of whale optimization algorithm (EWOA) is presented to be applied in adaptive control techniques as a parameter tuner. One weakness point in this control scheme is the low efficiency of its objective function. Balloon effect (BE) is a modification introduced to increase the efficiency of the objective function of the optimization method and the ability of the controller to deal with system problems increase consequently. Controlling of the temperature of electric furnaces is considered as one of the important issues in several industrial applications. Conventional controllers such as PID controller cannot deal efficiently with the problem of parameters variations and step disturbance. This paper proposes an adaptive controller, in which the gain of the temperature controller is tuned online using EWOA supported by balloon effect. System responses obtained by the proposed adaptive control scheme using EWOA + BE have been compared with an electric furnace temperature control (EFTC) scheme response using both the PID controller-based modified flower pollination algorithm (MoFPA) and PID-accelerated PIDA-based MoFPA. From the results, it can be observed that the proposed controller tuned by the EWOA + BE method improves the time performance compared with the other techniques (PID and PIDA-based MoFPA) in case of EFTC application.

Blockchain‐based secure information sharing for supply chain management: Optimization assisted data sanitization process

Currently, the furious competitiveness in global markets and speedy improvements in information technology lead to shorter product life cycles, lesser transportation capabilities, and increased demands as well. In most business scenarios, the supply chain network is becoming one of the most vital focusing areas. In the supply chain network, blockchain technology is a promising solution for secure information sharing. However, it is a bit critical in maintaining security at each level of the blockchain and hence the public–private–key cryptography is more commonly preferred. This study intends to construct a new privacy preservation model in the field of supply chain networks based on blockchain technology by undergoing three major phases, namely, (a) data sanitization, (b) key generation, and (c) restoration. Further, the sensitive fields in the original data are selected during the data sanitization phase, and in the key generation phase; the optimal key is generated to hide the selected sensitive fields. The hidden data with the secured key is transferred from the source (manufacturer) to destination (vendor) in the supply chain network via the blockchain. The restoration process takes place in the receiver side with the help of the same key. Among all these data flow methods, the optimal key selection is the critical issue that needs to be overridden to make the data transmission secured. As a novelty, a new optimization algorithm referred to as Whale with New Crosspoint-based Update (WNU), which is the advanced version of Whale Optimization Algorithm (WOA), is developed here to select the optimal key. Finally, the proposed WNU model is analyzed in terms of Hiding Failure (HF) rate, Information Preservation (IP) Rate, and False Rule generation (FR), and Degree of Modification (DM). The proposed secured information sharing in supply chain management (SCM) with blockchain technology will be validated by comparing it over the traditional models in terms of security as well.

Embedded chaotic whale survival algorithm for filter–wrapper feature selection

Classification accuracy provided by a machine learning model depends a lot on the feature set used in the learning process. Feature Selection (FS) is an important and challenging pre-processing technique which helps to identify only the relevant features from a dataset thereby reducing the feature dimension as well as improving the classification accuracy at the same time. The binary version of Whale Optimization Algorithm (WOA) is a popular FS technique which is inspired from the foraging behavior of humpback whales. In this paper, an embedded version of WOA called Embedded Chaotic Whale Survival Algorithm (ECWSA) has been proposed which uses its wrapper process to achieve high classification accuracy and a filter approach to further refine the selected subset with low computation cost. Chaos has been introduced in the ECWSA to guide selection of the type of movement followed by the whales while searching for prey. A fitness-dependent death mechanism has also been introduced in the system of whales which is inspired from the real-life scenario in which whales die if they are unable to catch their prey. The proposed method has been evaluated on 18 well-known UCI datasets and compared with its predecessors as well as some other popular FS methods.

An efficient terminal voltage control for PEMFC based on an improved version of whale optimization algorithm

Recently, the application of the proton exchange membrane fuel cells (PEMFCs) is extensively increasing as a popular renewable energy source. PEMFCs need low temperature for the operation along with high power density and easy implementation ability. These characteristics turned them into the most interesting type of fuel cells. However, PEMFCs need a technique for keeping them in a desired operating point. This is an essential item, especially during the current variation. In this study, a new configuration is proposed for designing an optimal PEMFC system. Two-phase interleaved boost DC/DC converter is also utilized for increasing the output voltage terminal. LQR optimal strategy is used for regulating the PEMFC. For improving the efficiency of the proposed LQR controller, a newly developed version of the whale optimization algorithm, called improved chaotic whale optimization algorithm is proposed. Simulation results of the proposed system are compared with different methods and the results showed that the proposed system has higher efficiency from the viewpoint of the current ripple and overshoot.

Meta-heuristic inspired by the behavior of the humpback whale tuned by a fuzzy inference system

The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE.

New brain tumor classification method based on an improved version of whale optimization algorithm

Brain tumor is an abnormal growth of cells in the brain that its diagnosis in the early stages can help us to prevent the dangers of the next stage. In this paper, a new meta-heuristic based methodology is presented for the early diagnosis of the brain tumor to prevent this objection. The proposed method includes three main phases including background removing, feature extraction, and classification based on multilayer perceptron neural network. Here, an improved model of the whale optimization algorithm based on the chaos theory and logistic mapping technique is employed to the optimal selection of the features and the classification stages. The performance analysis of the presented method is compared with some existing methods. Final results showed that based on analyzing CDR, FAR, and FRR as testifying indices, the proposed method has better results than the other similar methods.

Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique.

For some real-world problems, it is desirable to find multiple global optima as many as possible. The multimodal optimization approach which finds multiple optima in a single run shows significant difference with the single modal optimization approach.The whale optimization algorithm (WOA) is a newly emerging reputable optimization algorithm. Its global search ability has been verified in many benchmark functions and real-world applications. In this paper, we propose a multimodal version of whale optimization algorithm (MMWOA). MMWOA enhances the multimodal search ability of WOA by using the niching technique and improves the local search efficiency of WOA by combining the Gaussian sampling technique. The algorithm has been tested on multimodal optimization benchmark functions recommended by CEC'2013 and on a multimodal optimization problem with non-linear constraints. Experimental results indicate that MMWOA has competitive performance compared with other state-of-the-art multimodal optimization algorithms.

A novel parameter estimation in dynamic model via fuzzy swarm intelligence and chaos theory for faults in wastewater treatment plant

Faults during a wastewater treatment for plant (WWTP) are critical issue for social and biological. Poorly treated wastewater may achieve dangerous effect for human as well as nature. This paper proposed a novel model based on a binary version of whale optimization algorithm (WOA), chaos theory and fuzzy logic, namely (CF-BWOA). CF-BWOA is applied in the application of WWTP to find out the more relevant attributes from the whole dataset, reducing cost and validation of decision rules, and helping to identify a non-well-structured domain. CF-BWOA attempts to reduce the whole feature set without loss of significant information to the classification process. Fast fuzzy c-means is used as a cost function to measure the fuzzification and uncertainty of data. Ten different chaos sequence maps are used to estimate and tune WOA parameters. Experiments are applied on a complex real-time dataset with various uncertainty features and missing values. The overall result indicates that the CWOA with the Sine chaos map shows the better performance, lower error, higher convergence speed and shorter execution time. In addition, the proposed model is capable of detecting sensor process faults in WWTP with high accuracy and can guide the operators of these systems to control decisions.