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The slime mold algorithm (SMA) is a swarm-based metaheuristic algorithm inspired by the natural oscillatory patterns of slime molds. Compared with other algorithms, the SMA is competitive but still suffers from unbalanced development and exploration and the tendency to fall into local optima. To overcome these drawbacks, an improved SMA with a dyna...
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... design goal for extension/compression springs [56] is to obtain the minimum optimum weight under four constraints: deviation (g 1 ), shear stress (g 2 ), surge frequency (g 3 ), and deflection (g 4 ). As shown in Figure 4, three variables need to be considered. They are the wire diameter (d), the mean coil diameter (D), and the number of active coils (N). ...
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The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, th...
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... Naik et al. [22] proposed to add adaptive reverse learning at the later stage of iteration to avoid the premature end of convergence. Zhang et al. [23] presented reverse learning and Quantum Rotation Gate strategies to the SMA. Jiang et al. [24] proposed an improved SMA based on elite reverse learning. ...
This paper presents a hybrid algorithm based on the slime mould algorithm (SMA) and the mixed dandelion optimizer. The hybrid algorithm improves the convergence speed and prevents the algorithm from falling into the local optimal. (1) The Bernoulli chaotic mapping is added in the initialization phase to enrich the population diversity. (2) The Brownian motion and Lévy flight strategy are added to further enhance the global search ability and local exploitation performance of the slime mould. (3) The specular reflection learning is added in the late iteration to improve the population search ability and avoid falling into local optimality. The experimental results show that the convergence speed and precision of the improved algorithm are improved in the standard test functions. At last, this paper optimizes the parameters of the Extreme Learning Machine (ELM) model with the improved method and applies it to the power load forecasting problem. The effectiveness of the improved method in solving practical engineering problems is further verified.
... The position data produced by the swarm-based method is floating-point data, whereas quantum bits are binary. The discrete quantum bit data must be converted into continuous algorithm data to process the location information [36]. A quantum rotation gate rotates the data of each dimension of the search agent in pairs and updates it. ...
... The rotation angle of the QRG is chosen to maintain the balance between exploration and exploitation. When the current individual is far from the best, the value of θ should be raised during the early exploration stage whereas it should be lowered during the exploitation phase [36]. This approach makes it possible for the search process to adapt to various solutions and is better suited for searching for the overall best solution. ...
... The so-called group intelligence includes the behaviors of simple individuals and the whole group, exhibiting a specific intelligent feature without centralized control. Typical examples include ACO algorithm, PSO algorithm, artificial bee colony algorithm (ABC) [20], artificial fish swarming algorithm [21], grey wolf optimizer (GWO) [22], firefly algorithm (FA) [23], cuckoo search algorithm [24], chimp optimization algorithm [25], grasshopper optimization algorithm (GOA) [26], slime mold algorithm [27], and whale optimization algorithm (WOA) [28]. ...
Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s capability, the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms, including artificial bee colony algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm, grasshopper optimization algorithm, and whale optimization algorithm. The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems. DFA is applied to five engineering projects to demonstrate its applicability. The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms. Finally, potential upgrading routes for DFA are proposed as possible future developments.
... Wu et al. (2021) presented a chaotic whale optimization for object detection. Zhang et al.(2022) has proposed an improved slime mold optimization for global optimization and various engineering problems. Wang et al. (2021) introduced a hybrid aquila optimization for finding solution to engineering problems. ...
Enhancing the textural details in a satellite image play a significant part in satellite image processing. Satellite images are rich in textural and very minute spatial data. Failing to retrieve and enhance these critical details in the satellite image can lead to loss of information and hence poor results in the succeeding stages of satellite image processing. Correctly identifying the spatial and textural data in a satellite image is an effective way by which the image information can be preserved for a better-quality image. For this, the textural details should be distinguished, and then effective image processing can be performed. This paper introduces the Gabor filter-based parameter optimization for enhancing the textural and spatial information in the image. Manta ray foraging optimization is adopted for modifying the control parameters in the filter to account for the inadequacy of the algorithm in balancing the local and global search. A self adaptable Manta ray optimization is proposed, which is shown to outperform the traditional enhancement techniques such as Bilateral filter and Gabor filter optimized with Particle Swarm Optimization (PSO), Differential Evolution(DE) and Manta Ray Foraging Optimization(MRFO). The proposed method is compared with the traditional methods in terms of parameters such as Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM), Entropy and Computation time. The proposed method gave a huge improvement in PSNR by 17.61%, FSIM by 7.47% and entropy values by 6.7% and was seen to give the least CPU time.
... Moreover, the penalty costs are implemented and developed in the case of deadline violation. An enhanced SMA having a dynamic quantum rotation gate (DQRG) and opposition-based learning (OBL) was modelled in [17]. To be Specific, for the first time, 2 systems were utilized. ...
The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices. FL clients frequently use Wi-Fi and have to interact in unstable network surroundings. However, as the present FL aggregation approaches receive and send a large number of weights, accuracy can be decreased considerably in unstable network surroundings. Therefore, this study presents a Quantum with Metaheuristics Algorithm Based Minimization of Communication Costs in Federated Learning (QMAMCC-FL) technique. The presented QMAMCC-FL technique is designed a federated hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model with a quantum Aquila optimization (QAO) algorithm. The QMAMCC-FL technique upgrades the global model via weight collection of the learned model, which is commonly used in FL. The proposed model can be employed to increase the performance of network communication and reduce the size of data transmitted from clients to servers such as smartphones and tablets. The experimental analysis of the QMAMCC-FL approach is tested, and the outcomes show better performance over other existing models.
