An improved whale optimization algorithm with armed force program and strategic adjustment

To address the shortcomings of the whale optimization algorithm (WOA) in terms of insufficient global search ability and slow convergence speed, a differential evolution chaotic whale optimization algorithm (DECWOA) is proposed in this paper. Firstly, the initial population is generated by introducing the Sine chaos theory at the beginning of the algorithm to increase the population diversity. Secondly, new adaptive inertia weights are introduced into the individual whale position update formula to lay the foundation for the global search and improve the optimization performance of the algorithm. Finally, the differential variance algorithm is fused to improve the global search speed and accuracy of the whale optimization algorithm. The impact of various improvement strategies on the performance of the algorithm is analyzed using different kinds of test functions that are randomly selected. The particle swarm optimization algorithm (PSO), butterfly optimization algorithm (BOA), WOA, chaotic feedback adaptive whale optimization algorithm (CFAWOA), and DECWOA algorithm are compared for the optimal search performance. Experimental simulations are performed using MATLAB software, and the results show that the improved whale optimization algorithm has a better global optimization-seeking capability. The improved whale optimization algorithm is applied to the distribution network fault location of IEEE-33 nodes, and the effectiveness and accuracy of the distribution network fault zone location based on the multistrategy improved whale optimization algorithm is verified.

A rising meta-heuristic swarm intelligent computation algorithm, the Whale optimization Algorithm (WOA), which has been proved competitive with other intelligent optimization algorithms in solving different optimization problems. To accelerate the convergence rate of WOA, this paper has proposed a new improved whale optimization algorithm, called Chaos Regulation Whale optimization Algorithm (CRWOA). The chaotic map generator composed with three kinds of chaotic maps has been used to initialize the population. Furthermore, Cauchy distribution and Gauss distribution are introduced to regulate the position in each iteration. To analyze the superiority of CRWOA further, ten single-objective benchmark test functions are chosen. Through the comparison and analysis of the optimal results, CRWOA is demonstrated superior in general. It has faster convergence speed and better results than the other algorithms when less iteration number, less population quantity and wider ranges of variables are set.

To address the problems of local optima and insufficient convergence accuracy in parameter identification of primary frequency regulation (PFR) for steam turbines, this paper proposed a hybrid identification method that integrated an Improved Bayesian Optimization (IBO) algorithm and an Improved Whale Optimization Algorithm (IWOA). By initializing the Bayesian parameter population using Tent chaotic mapping and the reverse learning strategy, employing a radial basis kernel function hyperparameter training mechanism based on the Adam optimizer and optimizing the Expected Improvement (EI) function using the Limited-memory Broyden–Fletcher– Goldfarb–Shanno with Bounds (L-BFGS-B) method, IBO was proposed to obtain the optimal candidate set with the smallest objective function value. By introducing a nonlinear convergence factor and the adaptive Levy flight perturbation strategy, IWOA was proposed to obtain locally optimized optimal solutions. By using the reverse-guided optimization mechanism and employing a fitness-oriented selection strategy, the optimal solution was chosen to complete the closed-loop process of reverse learning feedback. Nine standard test functions and the Proportional Integral Derivative (PID) parameter identification of the electro-hydraulic servo system in a 330 MW steam turbine were presented as examples. Compared with Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Bayesian Optimization (BO) and Particle Swarm Optimization-Grey Wolf Optimizer (PSO-GWO), the Improved Bayesian Optimization-Whale Optimization Algorithm (IBO-WOA) proposed in this paper has been validated to effectively avoid the problem of getting stuck in local optima during complex optimization and has high parameter recognition accuracy. Meanwhile, an Out-Of-Distribution (OOD) Test based on noise injection had demonstrated that IBO-WOA had good robustness. The time constant identification of the steam turbine were carried out using IBO-WOA under two experimental conditions, and the identification results were input into the PFR model. The simulated power curve can track the experimental measured curve well, proving that the parameter identification results obtained by IBO-WOA have high accuracy and can be used for the modeling and response characteristic analysis of the steam turbine PFR.

To address the challenges of accurately modeling the motion of deep-sea aquaculture vessels under the combined effects of dynamic disturbances from the external environment and coupled disturbances from the liquid levels within the aquaculture tanks, as well as the performance limitations of traditional Active Disturbance Rejection Control (ADRC), this study proposes an improved Active Disturbance Rejection Control strategy based on the ADRC structure, and applies it to the design of the controller for the dynamic positioning system of aquaculture vessels. Furthermore, to tackle the issues of numerous uncertain parameters and tuning difficulties inherent in ADRC, an Improved Whale Optimization Algorithm (IWOA) is introduced for parameter tuning. The optimization algorithm incorporates a chaotic optimization strategy and an improved adaptive inertia weight ω with nonlinear characteristics, which simplifies the complex process of constructing an adaptive function in the Grey Wolf Optimization (GWO) algorithm and addresses the slow global search speed and poor local optimization ability of the traditional Whale Optimization Algorithm (WOA). The final Simulink simulation results demonstrate that the ADRC controller designed with IWOA outperforms traditional WOA, GWO, and Genetic Algorithm-optimized ADRC controllers in terms of control effectiveness and disturbance rejection capability, improved the dynamic positioning control performance of aquaculture vessels.

In order to solve the problems of slow convergence speed, insufficient accuracy, and easily falling into the local optimum of the traditional whale optimization algorithm (WOA) in mobile robot path planning, an improved whale optimization algorithm (IWOA) combined with the bird navigation mechanism was proposed. Specific improvement measures include using logical chaos mapping to initialize the population to enhance the randomness and diversity of the initial solution, designing a nonlinear convergence factor to prevent the algorithm from prematurely entering the shrinking surround phase and extending the global search time, introducing an adaptive spiral shape constant to dynamically adjust the search range to balance exploration and development capabilities, optimizing the individual update strategy in combination with the bird navigation mechanism, and optimizing the algorithm through companion position information, thereby improving the stability and convergence speed of the algorithm. Path planning simulations were performed on 30 × 30 and 50 × 50 grid maps. The results show that compared with WOA, MSWOA, and GA, in the 30 × 30 map, the path length of IWOA is shortened by 3.23%, 7.16%, and 6.49%, respectively; in the 50 × 50 map, the path length is shortened by 4.88%, 4.53%, and 28.37%, respectively. This study shows that IWOA has significant advantages in the accuracy and efficiency of path planning, which verifies its feasibility and superiority.

Aiming at the poor effect of traditional denoising algorithms on image enhancement with strong noise, an image denoising algorithm based on improved whale optimization algorithm and parameter adaptive array stochastic resonance is proposed in the paper. In this algorithm, through dimensionality reduction scanning, coding, modulation and other processing, the noise-containing gray image becomes a one-dimensional aperiodic binary pulse amplitude modulation signal suitable for a bistable stochastic resonance model. Then, the traditional whale optimization algorithm is improved in the initial solution distribution, global search ability and population diversity generalization. The improved whale optimization algorithm is applied to select the parameters of the stochastic resonance, which effectively improves the parameters self-adaptive of the array stochastic resonance model. Finally, the denoised image is obtained by demodulating, decoding and anti-scanning the stochastic resonance output. The experimental results show that compared with the array stochastic resonance method with fixed parameters and the classical image denoising method, the algorithm proposed in this paper has better performance in terms of visual effect and peak signal-to-noise ratio index, which proves the advantages and effective application of the method in image denoising.

Addressing the issues in traditional DQN algorithms for intelligent vessel path planning—such as routes overly close to obstacles, frequent sharp turns, slow convergence, overestimation of Q-values, and network instability— this paper proposes an intelligent ship path planning method (AS-DDQN) based on the DDQN algorithm. It combines Angle Searching with DDQN by pre-setting search angles to define search regions, thereby optimizing travel paths, reducing redundant exploration, and accelerating convergence. Additionally, supplementary reward functions are introduced to the traditional reward structure. These include rudder angle deviation penalties, safe distance penalties for the vessel's track, time penalties, and distance-based propulsion rewards. This ensures the vessel maintains a safe distance from obstacles while minimizing steering maneuvers. Finally, simulations were conducted in two distinct maritime environments near Zhoushan with varying complexity levels. Experimental results demonstrate that compared to the DDQN algorithm, the AS-DDQN algorithm significantly accelerates algorithm convergence. Moreover, across different maritime environments, the final planned routes achieved notable effectiveness in ensuring navigation safety and economic efficiency, better meeting the practical navigation requirements of vessels

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

The optimal scheduling of mine water is a multi-objective, multi-constraint, nonlinear, multi-stage combination of optimization problems, in view of the traditional solution methods with the increase in decision-making variable dimensions facing a large amount of computation, “dimensional disaster” and other problems, the introduction of a new intelligent simulation algorithm—the Whale Optimization Algorithm to solve the optimal scheduling problem of mine water. Aiming at the problem that the Whale Optimization Algorithm itself is prone to local optimization and slow convergence, it has been improved by improving its own parameters and introducing the inertia weight of the particle swarm and has achieved more obvious results. According to the actual situation of Nalinhe No. 2 Mine, the mathematical model of multi-target optimization of mine water is established based on the function of reuse time and reuse cost of mine water as the target function, and the balance of supply and demand of mine water, the water quality requirements of water use points at all levels, the water quantity requirements of reservoirs and the priority of water supply as the constraints. The improved Whale Optimization Algorithm was used to search optimal solution, and the results showed that the adaptability value of the improved Whale Optimization Algorithm was significantly improved compared with before, of which 8.65% and 7.69% were increased in the heating season and non-heating season, and the rate of cost reduction was 46.80% and 36.92%, and the iteration efficiency was also significantly improved, which improved the decision-making efficiency of optimal scheduling and became more suitable for the actual scheduling needs of Nalinhe No. 2 mine.

Whale optimization algorithm (WOA) is a novel-innovative swarm-based meta-heuristic algorithm with excellent performance, but it may still be trapped into local extremum for troublesome problems. To this end, an improved multi-objective whale optimization algorithm (IMOWOA) is proposed to cover the shortages. Firstly, in the search stage of WOA, individual difference is considered to strengthen the exploration ability, and evolution operators are introduced to regenerate the stagnated population to prevent premature convergence. Next, the performance of IMOWOA is compared with MOWOA and other classical optimization algorithms, and a series of multi-objective test functions are used. The results on the convergence and diversity of Pareto front confirm that IMOWOA has better feasibility and competitiveness. Finally, integrated with the least squares support vector regression (LSSVR) model, IMOWOA is applied to the deterministic optimization of vehicle structural crashworthiness. The conclusion testified the efficiency of IMOWOA in the field of vehicle crashworthiness.

In this paper, we have used a novel initialization strategy to improve Whale optimization algorithm (WOA), which is named as The Improved Whale Optimization Algorithm (IWOA). To evaluate the capability of the algorithm in terms of efficiency and performance, we have implemented it to solve thermal economic multi-objective optimization problems of Plate Fin Heat Exchanger (PFHE). We have investigated the design problem with a single-objective as well as multi-objectives. In single-objective we have minimized the total cost and maximized the effectiveness of PFHE. In multi-objective, we have combined the total cost and effectiveness, with the help of design weights and a penalty parameter. The sensitivity of IWOA is checked towards the change in population sizes and the target prey numbers. The algorithm was stable in calculating the best values but was variative in number of functions evaluations. The performance of IWOA is compared with Genetic Algorithm (GA), Elitist-Jaya Algorithm (EJA), and modified-TLBO (Teaching Learning Based Optimization). Which show that IWOA has significantly improved the results. The suggested algorithm has less parameters to be set by designers. It converges to the required results quickly and is easy to implement. Similarly, all the experiments suggested that IWOA is applicable to design problems with complex objectives and highly non-linear constraints.

Path planning for solar-powered UAV in urban environment

The removal of irrelevant and insignificant features from the high-dimensional dataset is a necessary prerequisite for the exploration of information. Meta-heuristic optimization techniques have been widely used in the field of knowledge discovery over the last few years. The whale optimization algorithm (WOA) is a swarm-based metaheuristic technique that is often used in the field of dimensionality reduction. Among the various WOA-based feature selection techniques in the literature, not a single technology illuminates the stability issue of WOA. Stability is often identified as a sensitivity to the disruption of input data during the process of selecting significant features. In this study, a new feature selection model based on improved WOA (iWOA) is proposed to select significant features from a high-dimensional microarray dataset. The stability of the results obtained is evaluated with the existing stability index that satisfies all the required characteristics of the stability measure. In addition, the results of the proposed model are compared with other contemporary meta-heuristics techniques. The proposed iWOA proposes its identification as a well-stable feature selection technique according to the strength of the stability index agreement.

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.

Prediction of coal and gas outburst hazard using kernel principal component analysis and an enhanced extreme learning machine approach