Standard Whale Optimization Algorithm Research Articles

Hybrid Mixture Model Based on a Hybrid Optimization for Spectrum Sensing to Improve the Performance of MIMO–OFDM Systems

Cognitive radio (CR) is the trending domain in addressing the inadequate bands for communication, and spectrum sensing is the hectic challenge need to be addressed extensively. In the conventional CRs, the communication is restricted to the secondary users (SUs) in the allocated bands causing the underutilization of the available band. Thus, with the aim to afford higher throughput and spectrum efficiency, this paper introduces the hybrid mixture model for spectrum sensing in the multiple-input–multiple-output (MIMO) systems and the effectiveness is evaluated based on the evaluation parameters, such as detection probability and probability of false alarm. The signal received through the orthogonal frequency-division multiplexing (OFDM) antenna is employed for analyzing the spectral availability for which the energy and Eigen statistics of the signal is generated, which forms the input to the Hybrid mixture model. The developed Hybrid mixture model is the integration of the Gaussian Mixture Model (GMM) and Whale Elephant-Herd Optimization (WEHO). The GMM is subjected to the optimal tuning using the WEHO, which is the modification of the standard Whale Optimization Algorithm (WOA) with the Elephant-Herd Optimization (EHO). The analysis reveals that the proposed spectrum sensing model acquired the maximal detection probability and minimal false alarm probability of 99.9% and 46.4%, respectively. The proposed hybrid mixture model derives the spectrum availability and ensures the effective communication in CR without any interference.

A guided population archive whale optimization algorithm for solving multiobjective optimization problems

This paper proposes a new multiobjective algorithm by extending the recently Whale Optimization Algorithm (WOA) to solve multiobjective optimization problems. Our algorithm which we shall call Guided Population Archive Whale Optimization Algorithm (GPAWOA) is based on Pareto dominance and uses an external archive to store the non-dominated solutions found during the optimization process. The leaders are selected from the archive to guide the population towards promising regions of the search space, also, the mechanism of crowding distance is incorporated into the standard WOA to maintain the diversity. Our proposed algorithm is evaluated on twelve benchmark functions and is applied to four multi-objective engineering design problems: 4-bar truss design, gear train problem, disk brake design and welded beam design and compared against three well-known algorithms: Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), Multi-Objective Grey Wolf Optimizer (MOGWO), and Multi-Objective Particle Swarm Optimization (MOPSO). The experimental results indicate that the proposed GPAWOA is highly competitive and outperforms the selected state-of-the-art multiobjective optimization algorithms, being able to provide an excellent approximation of Pareto front in terms of convergence and diversity.

Towards locating time-varying indoor particle sources: Development of two multi-robot olfaction methods based on whale optimization algorithm

Source localization is crucial for controlling indoor particle pollution. Locating indoor particle sources is challenging because the dispersion of particles is more complicated than that of gases, and the release rates of particle sources usually change with time in real-world applications. This study presents two multi-robot olfaction methods based on the newly emerging whale optimization algorithm (WOA), namely, the standard WOA (SWOA) and improved WOA (IWOA) methods, for locating time-varying indoor particle sources without and with airflow information, respectively. By combining experiments and CFD simulations, the presented methods were validated and compared with two particle swarm optimization (PSO)-based methods, namely, standard PSO (SPSO) and improved PSO (IPSO) methods. Four typical scenarios, including two time-varying source types (decaying source and periodic source) and two ventilation modes (displacement ventilation and mixing ventilation), were simulated and exported as virtual environments to test these methods. The methods were evaluated by the success rate (the number of successful experiments divided by the number of total experiments) and the average localization time of the experiments. The results showed that the SWOA method outperformed the SPSO method with a higher success rate (SWOA: 66.00%, SPSO: 52.00%) and a less average localization time (SWOA: 65.48 s, SPSO: 69.65 s) for all four scenarios. The IWOA method performed slightly better in success rate (IWOA: 97.75%, IPSO: 97.00%), while the IPSO method performed slightly better in average localization time (IWOA: 42.18 s, IPSO: 39.18 s) for all four scenarios. In addition, the most cost-effective anemometer was also determined.

Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure

In the case of controlling the seismic vibration of a structure, reliance on human knowledge and expert in the formulation of a fuzzy logic controller leads to non-optimal solutions, which makes the use of control devices and algorithm unreasonable. In most cases, the calculated control force for high-rise buildings is very large and the controlled response of the structure is not significantly decreased. To overcome these drawbacks, the parameter tuning of fuzzy systems with optimization algorithms are necessary. This paper focuses on the optimization of a fuzzy controller applied to a seismically excited nonlinear steel building. In the majority of cases, this problem is formulated based on the structural responses in linear range, however in this paper, objective functions and the performance criteria are considered with respect to the nonlinear responses of the structure. An Upgraded Whale Optimization Algorithm is proposed and utilized as the optimization technique for parameter tuning of the fuzzy controller. The performance of the presented upgraded algorithm is compared with the standard Whale Optimization Algorithm and eight different metaheuristic algorithms. The obtained results prove that the upgraded method is capable of providing competitive results.

Dynamic scheduling applying new population grouping of whales meta-heuristic in cloud computing

Scheduling in cloud computing is the assignment of tasks to resources with maximum performance, which is a multi-purpose problem. The scheduling is of NP-Hard issues that is the reason why meta-heuristic algorithms are used in scheduling problems. The meta-heuristic scheduling algorithms are divided into two categories of biological and non-biological. Swarm-based meta-heuristics are of biological algorithms that are based on imitation, or based on sign. The whale optimization algorithm is a meta-heuristic biological swarm-based intelligence algorithm based on imitation. This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the Whale optimization algorithm is introduced that presents a new idea in grouping whales called GWOA. It is firstly proposed to overcome the early convergence problem and then make a balance between the local and the global search in finding the optimal solution. The proposed method divides the sorted population into δ groups and a member of each group is randomly selected which is used in encircling prey section of the whale optimization algorithm. Then, the average best fitness was enhanced to improve both exploitation and exploration as well as premature convergence. In the next step, GWOA is used in a cloud computing scheduler at high workload to reduce the average execution time, response time, and increase the throughput in the cloud computing environment. The proposed whale optimization algorithm is compared with the standard whale optimization algorithm (WOA), improved whale optimization algorithm (CWOA), particle swarm optimization (PSO), and bat algorithms applying CEC2017 functions to compare the average parameter of the best fit, and then they are implemented as a cloud computing scheduler. The results of the experiments show that the proposed method has a better performance in comparison with competent meta-heuristic algorithms and scheduling algorithms.

The Exploration/Exploitation Tradeoff in Whale Optimization Algorithm

The whale optimization algorithm(WOA) is a novel meta-heuristic evolutionary algorithm inspired by the behavior of whales predation. An important factor to the success of WOA is the balancing between exploration and exploitation. In the WOA, the distance control parameter $a$ is the main factor to find an appropriate balance between exploration and exploitation. In the standard WOA, the distance control parameter $a$ is optimized by linear control strategy (LCS), but the process of whales predation is not simply linear process. To address the issue, this paper proposed a nonlinear control strategy based on arcsine function (NCS-Arcsin) to optimize WOA. The NCS-Arcsin is applied to adjust distance control parameter $a$ . The NCS-Arcsin is considered to accurately describe the process of whales predation. Experiments on twelve well-known benchmark functions show the NCS-Arcsin can significantly improve the exploration and exploitation capabilities of WOA. In addition, the performance of proposed NCS-Arcsin is compared with LCS and other have been proposed NCS. The experimental results show that the optimization effect of NCS-Arcsin is stronger than that of LCS and other NCS. The NCSs based on the arcsine function is the best NCS, which can significantly improve the optimize performance of WOA.

Research on limited buffer scheduling problems in flexible flow shops with setup times

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.

Research on limited buffer scheduling problems in flexible flow shops with setup times

In order to solve the limited buffer scheduling problems in flexible flow shops with setup times, this paper proposes an improved whale optimization algorithm (IWOA) as a global optimization algorithm. Firstly, this paper presents a mathematic programming model for limited buffer in flexible flow shops with setup times, and applies the IWOA algorithm as the global optimization algorithm. Based on the whale optimization algorithm (WOA), the improved algorithm uses Levy flight, opposition-based learning strategy and simulated annealing to expand the search range, enhance the ability for jumping out of local extremum, and improve the continuous evolution of the algorithm. To verify the improvement of the proposed algorithm on the optimization ability of the standard WOA algorithm, the IWOA algorithm is tested by verification examples of small-scale and large-scale flexible flow shop scheduling problems, and the imperialist competitive algorithm (ICA), bat algorithm (BA), and whale optimization algorithm (WOA) are used for comparision. Based on the instance data of bus manufacturer, simulation tests are made on the four algorithms under variouis of practical evalucation scenarios. The simulation results show that the IWOA algorithm can better solve this type of limited buffer scheduling problem in flexible flow shops with setup times compared with the state of the art algorithms.

Biped robot stability based on an A–C parametric Whale Optimization Algorithm

The easy gait stability of biped robot is an important issue and has been mentioned in different works in literature. To evaluate the walking stability, we performed zero moment point (ZMP) analysis for the obtained trajectory model. Whale Optimization Algorithm (WOA) has been gained more interest, due to the fewer number of control parameters, and easy implementation of the code. However, WOA has low convergence speed and accuracy. In this paper, a new variant of WOA which focuses on balancing between exploration and exploitation is proposed. The proposed algorithm named as the A–C parametric WOA targets the A and C parameters of the standard WOA specifically through variation of “a” parameter non-linearly and randomly, as well as updating parameter “C” by applying inertia weight strategy. To verify the performance of the proposed A–C parametric WOA, we at first test it against the standard WOA using 41 benchmark functions from CEC’2005 and CEC’2017. The results show a success rate of 38 out of 41 functions, while the T-test and Wilcoxon analyses succeeded in 33 and 37 out of 41 respectively. Secondly, a comparative study has been held between the A–C parametric WOA and the most well-known state-of-art soft computing techniques (PSO, DE, CS, GWO, WOA, and AGWO) through the standard deviation (STD) and the average resulting in a success rate of 6 out of 8 common benchmark functions. Thirdly, the parametric A–C WOA is applied on a biped robot to find the optimal settings of the hip parameters that make ZMP stays in the middle of the support polygon as much as possible. A comparative study has been held between the proposed algorithm and various well-known algorithms (PSO, DE, GA, WOA, and SSA) resulting in the best convergence characteristic with a reduction in STD of 47.9% compared to the standard WOA. Results show that the proposed A–C parametric WOA has the best results with the lowest STD and minimum error.

Chaotic whale optimizer variants for parameters estimation of the chaotic behavior in Permanent Magnet Synchronous Motor

Permanent Magnet Synchronous Motor (PMSM) is widely used in the industrial applications because of its simple construction and its high efficiency. Unfortunately, if a small change has occurred in the system parameters, the PMSM displays an unexpected performance which is known as a chaotic behavior. To control this undesirable behavior, it is crucial to propose a robust tool to extract the model parameters accurately and rapidly. In this work, novel developed optimization techniques called Chaotic Whale Optimization variants (CWOA) are proposed where the standard Whale Optimization Algorithm (WOA) is integrated with different ten chaos maps to tune some of its parameters. Four CWOA variants are introduced, tested and validated mathematically over CEC 2017. In addition, CWOA variants and the standard WOA version are proposed to estimate the parameters of the chaotic behavior in PMSM at both of the off-line and the on-line operating conditions without and with noise. The results of the proposed algorithms are compared with that of the previous techniques in literature to test their efficiency, reliability, and accuracy. An intensive statistical analysis is accomplished for extra validation of the superiority of the proposed variants. The overall results indicate that the CWOA-II variant with the logistic chaos map offers the best performance among all other variants where it provides lower error values between the estimated and the original system performance, higher convergence speed and shorter execution time. This eventually helps to provide an accurate and fast description of the chaotic region to ensure a fast control of the motor as well as a fast protection from damage.

Integrating the whale algorithm with Tabu search for quadratic assignment problem: A new approach for locating hospital departments

The Quadratic Assignment Problem (QAP) is a combinatorial NP-hard optimization problem that is not solvable in a polynomial time. It has a large number of real-world applications in diverse fields (e.g. facility arrangement in a hospital). The Whale Optimization Algorithm is a new meta-heuristic that achieves a great success in solving the continuous problems. In this paper, we propose a memetic algorithm using the Whale optimization Algorithm (WA) Integrated with a Tabu Search (WAITS) for solving QAP. In fact, this work employs Tabu Search to improve the quality of solution obtained by WA for QAP problem as a local search algorithm. This is an attempt to improve the convergence speed and local search of WA as its main drawbacks. Due to the combinatorial nature of QAP, the continuous values generated from the standard WA were converted to discrete values by the largest real value mapping. The WAITS algorithm is enhanced by a local search that defines a set of neighborhood solutions to improve the accuracy of the obtained solutions. Fourteen different case studies including 122 test problems are employed for analyzing the performance of the proposed WAITS. The results show that the proposed memetic algorithm finds near-optimal solutions with an acceptable computational time. WAITS is compared to several algorithms in the literature. The results show that the proposed algorithm outperforms similar algorithms in the literature.

An Improved Whale Optimization Algorithm Based on Different Searching Paths and Perceptual Disturbance

Whale optimization algorithm (WOA) is a swarm intelligence optimization algorithm inspired by humpback whale hunting behavior. WOA has many similarities with other swarm intelligence algorithms (PSO, GWO, etc.). WOA’s unique search mechanism enables it to have a strong global search capability while taking into account the strong global search capabilities. In this work, considering the the deficiency of WOA in local search mechanism, combined with the optimization methods of other group intelligent algorithms, perceptual perturbation mechanism is introduced, which makes the agent perform more detailed searches near the local extreme point. At the same time, since the WOA uses a logarithmic spiral curve, the agent cannot fully search all the spaces within its search range, even though the introduction of the perturbation mechanism may still lead to the algorithm falling into a local optimum. Therefore, the equal pitch Archimedes spiral curve is chosen to replace the classic logarithmic spiral curve. In order to fully verify the effect of the search path on the performance of the algorithm, several other spiral curves have been chosen for experimental comparison. By utilizing the 23 benchmark test functions, the simulation results show that WOA (PDWOA) with perceptual perturbation significantly outperforms the standard WOA. Then, based on the PDWOA, the effect of the search path on the performance of the algorithm has been verified. The simulation results show that the equal pitch of the Archimedean spiral curve is best.

Novel Adaptive Whale Optimization Algorithm for Global Optimization

Background/Objectives: In the meta-heuristic algorithms, randomization plays a very crucial role in both exploration and exploitation. So meta-heuristic algorithms are proposed to avoid these problems. Methods/Statistical Analysis: A novel bio-inspired optimization algorithm based on the special bubble-net hunting strategy used by humpback whales called the Whale Optimization Algorithm (WOA). In contrast to meta-heuristic, main feature is randomization having a relevant role in both exploration and exploitation in optimization problem. A novel randomization technique termed adaptive technique is integrated with WOA and exercised on ten unconstraint test benchmark function. Findings: WOA algorithm has quality feature that it uses logarithmic spiral function so it covers a broader area in exploration phase then addition with powerful randomization adaptive technique potent the adaptive whale optimization Algorithm (AWOA) to attain global optimal solution and faster convergence with less parameter dependency. Application/Improvements: Adaptive WOA (AWOA) solutions are evaluated and results shows its competitively better performance over standard WOA optimization algorithm.