Cloud task scheduling is a critical optimization problem in cloud computing environments, aiming to allocate computational tasks to appropriate virtual machines while reducing execution time, balancing resource load, and minimizing scheduling cost. However, due to the high dimensionality, nonlinear characteristics, and complex constraints of cloud scheduling scenarios, traditional optimization methods often struggle to obtain high-quality solutions efficiently. To address these challenges, this paper proposes a Multi-strategy Improved Golden Sine Optimization Algorithm (MIGoldSA) for global optimization and cloud task scheduling problems. First, an adaptive chaotic opposition initialization strategy is incorporated to improve the distribution quality and diversity of the initial population. Second, a dynamic elite-guided sine evolution strategy is designed to reduce the dependence on a single best individual and improve the coordination between global exploration and local exploitation. Third, an Economic Investment Strategy is introduced to adaptively allocate search efforts according to the optimization potential of individuals. To verify the effectiveness of MIGoldSA, extensive experiments are conducted on the IEEE CEC2017 and CEC2022 benchmark suites and compared with nine advanced optimization algorithms. The results show that MIGoldSA obtains the best or tied-best mean fitness values on 60 out of 84 benchmark cases, accounting for 71.43% of all test cases. In the Wilcoxon signed-rank test, MIGoldSA achieves 662 wins, 57 ties, and 37 losses among 756 pairwise comparisons, corresponding to an overall win rate of 87.57% and a non-inferiority rate of 95.11%. In addition, the Friedman mean ranks of MIGoldSA are 1.47, 2.00, 3.98, and 4.17 under the four benchmark settings, which are reduced by 85.26%, 79.94%, 45.25%, and 42.32%, respectively, compared with the original GoldSA. Furthermore, the proposed algorithm is applied to cloud task scheduling problems under different task scales. The experimental results show that MIGoldSA maintains competitive time-cost performance and achieves clear reductions in load cost, price cost, and comprehensive scheduling cost. Compared with the original GoldSA, the normalized comprehensive scheduling cost is reduced by approximately 9–14% in small-scale scenarios and approximately 18–21% in large-scale scenarios. Meanwhile, the normalized load cost and price cost are reduced by about 18–25% and 10–18%, respectively, and the time cost shows an approximately 8–12% reduction in large-scale scheduling scenarios. These quantitative results demonstrate that MIGoldSA can improve the optimization accuracy, statistical robustness, and overall scheduling cost efficiency of the original GoldSA on most tested problems.
With the increasing complexity of engineering optimization and intelligent decision-making problems, traditional metaheuristic algorithms often suffer from premature convergence, loss of population diversity, and insufficient adaptability to complex fitness landscapes. To address these issues, this paper proposes a Multi-strategy Symmetry-Aware Improved Golden Sine Algorithm (MIGoldSA). The proposed algorithm introduces a symmetry-guided multi-strategy framework in which multiple complementary search operators are organized in a structurally balanced manner. Specifically, a strategy pool consisting of the original golden sine update rule, three differential evolution mutation strategies, and an elite-based quadratic interpolation local search operator is constructed. An adaptive strategy selection mechanism is further developed to dynamically regulate the selection probabilities of different strategies according to their historical success rates, forming a dynamic probabilistic symmetry that balances global exploration and local exploitation throughout the optimization process. The numerical performance of the resulting method is assessed using the CEC2014, 30-dimensional CEC2017, and 20-dimensional CEC2022 test collections. Comparative and statistical findings confirm that MIGoldSA generally delivers more accurate final solutions, more consistent outcomes across independent trials, and stronger convergence behavior than established algorithms and recently developed competitors. Its applicability is further examined in corporate insolvency forecasting by employing MIGoldSA to determine the hyperparameter configuration of a K-nearest neighbors classifier. Tests conducted on the Wieslaw financial database show that the resulting MIGoldSA-KNN system outperforms the selected reference models in classification accuracy, Matthews correlation coefficient, F1-score, and recall. These findings suggest that the proposed symmetry-inspired architecture offers an effective means of coordinating diversified search and intensive refinement, thereby providing a valuable computational approach for challenging global optimization and financial classification tasks.
A Adaptive Genetic Algorithm (AGA) is designed to solve the Multi-Objective Flexible Job Shop Green Scheduling Problem (MO-FJGSP), which aims to minimize the makespan, total energy consumption, and total carbon emissions.
Ming-Yue Li, Lina Wang, Jun Wang et al.· Journal of Engineering, Proj...· 0 citations
Complex nonlinear problems are typically solved by optimization methods, and metaheuristic algorithms have attracted significant attention because of their ability to solve complex optimization problems. Most of the available approaches, however, have weaknesses, including premature convergence, a lack of balance between exploration and exploitation and slow convergence within high-dimensional spaces. To overcome these shortcomings, in this paper, the hybridization of the sine cosine algorithm (SCA) and butterfly optimization algorithm (BOA) and the hybrid sine cosine-butterfly optimization algorithm (SCA-BOA), which have the global exploration power of the sine cosine algorithm and the high local exploitation power of the butterfly optimization algorithm, are suggested. The suggested solution works in two phases. In the first step, the sine cosine mechanism improves the diversity in the population and conducts a global search with sinusoidal operators. At a later stage, the butterfly optimization strategy increases the search near promising regions, enhancing local refinement. This hybrid design permits a more balanced trade-off between exploration and exploitation as well as improved convergence stability. The proposed algorithm was evaluated using classical benchmark functions and the IEEE CEC 2022 benchmark suite under identical experimental settings. Ten standard benchmark functions were tested using the algorithm and compared to nine existing metaheuristic algorithms under the same experimental conditions. The findings show that the SCA-BOA has better performance in terms of fitness values, convergence speed, and stability of the solution. The experimental results demonstrate that the proposed hybrid SCA-BOA achieved the best overall average ranking score (2.3) among the compared optimization algorithms and produced competitive mean fitness and standard deviation values across diverse benchmark landscapes. Furthermore, the proposed algorithm was validated using the IEEE CEC 2022 benchmark suite, where convergence analysis demonstrated improved convergence stability and adaptive search behavior on unimodal, multimodal, hybrid, and composition benchmark functions. In addition, population diversity analysis quantitatively confirmed the balanced exploration–exploitation capability of the proposed hybrid algorithm and its effectiveness in avoiding premature convergence during optimization. These results demonstrate the effectiveness of the proposed algorithm and its ability to address complex continuous optimization problems in the field.
A hybrid scheduling framework that integrates Hybrid Wild Goose Optimization (HWGO) with Deep Reinforcement Learning (DRL) is investigated, indicating that intelligent hybrid optimization techniques can provide adaptive and efficient task scheduling solutions for modern cloud computing environments.
Annaiah H, A. Rajesh· International journal of com...· 0 citations
Resource optimization is an essential factor in many areas, such as manufacturing, logistics, cloud computing, wireless sensor network and energy systems. Conventional optimization methods can be impractical when utilized in large, nonlinear, or dynamic optimization problems. Swarm Intelligence (SI) algorithms are based on collective behavior in nature, which provide alternatives, which are decentralized, flexible, and efficient. This paper provides a detailed experimental analysis of three traditional swarm algorithms Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC) to an allocation of resources and scheduling. The performance of each algorithm was determined using a benchmark problem, which modeled the problem of minimizing costs, resource utilization, convergence behavior, the complexity of computation and robustness to dynamic problems. Furthermore, a hybrid PSO-ACO algorithm is suggested with the utilization of the fast convergence of PSO and the global search of ACO. The observed experimental results can confirm that the hybrid model is more effective in cost optimization and utilization by 12-18 percent than other algorithms. The paper ends by projecting the research directions in the future such as adaptive hybridization, multi-objective optimization, and real-world applications.
Mandeep Kaur· International journal of com...· 0 citations
A novel hybrid approach combining Bee Colony Optimization and Genetic Algorithm for efficient task scheduling in multi-core processor systems that leverages the global exploration capabilities of BCO and the exploitation strengths of GA to achieve optimal task-to-core assignments while minimizing makespan and balancing system load is proposed.
C. Igiri, Victor Peters, Igu Ajumoke Elizabeth· Journal of Artificial Intell...· 0 citations
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