Strong Optimization Capability Research Articles

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

IOOA: A multi-strategy fusion improved Osprey Optimization Algorithm for global optimization

<abstract><p>With the widespread application of metaheuristic algorithms in engineering and scientific research, finding algorithms with efficient global search capabilities and precise local search performance has become a hot topic in research. The osprey optimization algorithm (OOA) was first proposed in 2023, characterized by its simple structure and strong optimization capability. However, practical tests have revealed that the OOA algorithm inevitably encounters common issues faced by metaheuristic algorithms, such as the tendency to fall into local optima and reduced population diversity in the later stages of the algorithm's iterations. To address these issues, a multi-strategy fusion improved osprey optimization algorithm is proposed (IOOA). First, the characteristics of various chaotic mappings were thoroughly explored, and the adoption of Circle chaotic mapping to replace pseudo-random numbers for population initialization improvement was proposed, increasing initial population diversity and improving the quality of initial solutions. Second, a dynamically adjustable elite guidance mechanism was proposed to dynamically adjust the position updating method according to different stages of the algorithm's iteration, ensuring the algorithm maintains good global search capabilities while significantly increasing the convergence speed of the algorithm. Lastly, a dynamic chaotic weight factor was designed and applied in the development stage of the original algorithm to enhance the algorithm's local search capability and improve the convergence accuracy of the algorithm. To fully verify the effectiveness and practical engineering applicability of the IOOA algorithm, simulation experiments were conducted using 21 benchmark test functions and the CEC-2022 benchmark functions, and the IOOA algorithm was applied to the LSTM power load forecasting problem as well as two engineering design problems. The experimental results show that the IOOA algorithm possesses outstanding global optimization performance in handling complex optimization problems and broad applicability in practical engineering applications.</p></abstract>

Performance prediction of the centrifugal air compressor for fuel cells considering degradation characteristics based on the hierarchical evolutionary model

The performance of the centrifugal air compressor is closely related to the efficiency and energy density of the fuel cell system, and extensive work has been done in this study to achieve precise performance predictions of the compressor considering degradation characteristics. The durability test of the compressor was carried out for 5000 h, and degradation characteristics with break-in time were discussed in detail. On this basis, the hierarchical evolutionary model was constructed to perform the performance predictions based on the Light Gradient Boosting Machine (LightGBM) and Multi-objective Whale Optimization Algorithm (MOWOA). The results demonstrate that the outlet pressure of the compressor decreases by 9.4 kPa and the power consumption increases by 0.71 kW at rated volume flow rate after the break-in of 5000 h, with attenuation ratios of 4.7 % and 12.2 %, respectively. The final non-dominated solution sets have excellent uniformity, diversity and convergence, proving that the developed MOWOA has high adaptability and strong optimization capability. The hierarchical evolutionary model achieves exact predictions for the outlet temperature, flow rate and pressure of the compressor, with absolute relative errors (AREs) below 1 % in outlet temperature and pressure while 5 % in outlet flow rate. All of these provide an efficient multi-objective optimization algorithm and an auto-calibrated hierarchical evolutionary model for further performance optimization of the air compressor.

PID-based search algorithm: A novel metaheuristic algorithm based on PID algorithm

In this paper, a metaheuristic algorithm called PID-based search algorithm (PSA) is proposed for global optimization. The algorithm is based on an incremental PID algorithm that converges the entire population to an optimal state by continuously adjusting the system deviations. PSA is mathematically modeled and implemented to achieve optimization in a wide range of search spaces. PSA is used to solve CEC2017 benchmark test functions and six constrained problems. The optimization performance of PSA is verified by comparing it with seven metaheuristics proposed in recent years. The Kruskal-Wallis, Holm and Friedman tests verified the superiority of PSA in terms of statistical significance. The results show that PSA can be better balanced exploration and exploitation with strong optimization capability. Source codes of PSA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/131534-pid-based-search-algorithm.

Improved Salp Swarm Algorithm for Tool Wear Prediction

To address the defects of the salp swarm algorithm (SSA) such as the slow convergence speed and ease of falling into a local minimum, a new salp swarm algorithm combining chaotic mapping and decay factor is proposed and combined with back propagation (BP) neural network to achieve an effective prediction of tool wear. Firstly, the chaotic mapping is used to enhance the formation of the population, which facilitates the iterative search and reduces the trapping in the local optimum; secondly, the decay factor is introduced to improve the update of the followers so that the followers can be updated adaptively with the iterations, and the theoretical analysis and validation of the improved SSA are carried out using benchmark test functions. Finally, the improved SSA with a strong optimization capability to solve BP neural networks for the optimal values of hyperparameters is used. The validity of this is verified by using the actual tool wear data set. The test results of the benchmark test function show that the algorithm presented has a better convergence speed and solution accuracy. Meanwhile, compared with the original algorithm, the R2 value of the part life prediction model proposed is improved from 0.962 to 0.989, the MSE value is reduced from the original 34.4 to 9.36, which is a 72% improvement compared with the original algorithm, and a better prediction capability is obtained.

Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection

The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy to understand and has a strong optimization capability. However, the SMA is not suitable for solving multimodal and hybrid functions. Therefore, in the present study, to enhance the SMA and maintain population diversity, a dispersed foraging SMA (DFSMA) with a dispersed foraging strategy is proposed. We conducted extensive experiments based on several functions in IEEE CEC2017. The DFSMA were compared with 11 other meta-heuristic algorithms (MAs), 10 advanced algorithms, and 3 recently proposed algorithms. Moreover, to conduct more systematic data analyses, the experimental results were further evaluated using the Wilcoxon signed-rank test. The DFSMA was shown to outperform other optimizers in terms of convergence speed and accuracy. In addition, the binary DFSMA (BDFSMA) was obtained using the transform function. The performance of the BDFSMA was evaluated on 12 datasets in the UCI repository. The experimental results reveal that the BDFSMA performs better than the original SMA, and that, compared with other optimization algorithms, it improves classification accuracy and reduces the number of selected features, demonstrating its practical engineering value in spatial search and feature selection.

Parameter configuration of composite vibration isolation system using power flow and MOPSO technique

Traditional vibration isolation design often aims to suppress vibrations from machinery equipment, while vibrations in the foundation are usually ignored. In this paper, the “machinery equipment – single-stage (double-stage) isolators – thin plate foundation ” is considered as a composite vibration isolation system, with equipment deployed at four positions on the plate. The forces transmitted from the equipment to the foundation were derived using a mechanical four-pole connection principle, and the displacement admittance of the junctions between the equipment and the plate was used as the key intermediate variable. Multi-objective optimization for the proposed system was subsequently performed, using fitness functions to reduce the maximum power flow transmitted to the plate and ensure a uniform vibration of the machinery equipment. A multi-objective particle swarm optimization (MOPSO ) algorithm was used as the optimization tool which offers the advantages of fewer parameter settings, fast convergence, a strong optimization capability, and possession of a unique, global and optimal solution (gbest solution) based on Pareto dominance. The power flow that induced plate vibrations from the machinery isolation system was introduced in this study, which was used to consider the transmitted force and velocity on the plate simultaneously. An advanced theory of clamped plate vibration aptly supported this strategy, and inspiration was also provided by an innovative optimization strategy based on artificial intelligence.

A Hybrid Intelligent Optimization Algorithm of Fast Convergence

A hybrid intelligent optimization algorithm based on quantum particle swarm is presented to solve the problem that the local search ability of traditional SFLA is poor and converges very slowly. The particle is quantized and introduced chaos mechanism in the algorithm in order to enhance the global search ability, using the escape strategy, the group is divided into three clusters and mutation operation on the cluster within individuals, not only improves the convergence speed and ensure the performance of the algorithm. Experiments show that the improved algorithm has the characteristics of strong optimization capability and performance is improved greatly in whether comparison of the baseline function or analysis of universal database, compared with the other two algorithms have obvious advantages.