ABSTRACT The increasing application of unmanned aerial vehicles (UAVs) in diverse domains demands highly robust and autonomous path‐planning algorithms capable of navigating complex and dynamic environments. To address the multifaceted challenges posed by obstacle avoidance, energy constraints, and environmental uncertainty, this work proposes an ensemble of learning strategies for the optimal path planning of UAVs. We introduce a modular particle swarm optimization and differential evolution (PSO‐DE) ensemble framework and systematically investigate the impact of multiple learning and adaptation strategies, such as chaotic parameter adaptation, opposition‐based learning (OBL), and a range of DE mutation schemes, to enhance the optimization process. We perform extensive experimentation across 16 carefully designed scenarios with varying complexity against ten competitive algorithms. We demonstrate that the integration of the PSO‐DE hybrid with the opposition‐based learning (OBLPSODE) achieves faster convergence while maintaining superior solution quality across all scenarios. The proposed OBLPSODE algorithm substantially outperforms other hybrid variants in both computational efficiency and path optimality, particularly excelling in cluttered environments where traditional algorithms often converge prematurely. Beyond algorithmic contributions, this work provides critical complexity analysis identifying obstacle‐checking operations as the primary computational bottleneck in UAV path planning. The findings offer practical guidance for deploying UAVs in real‐world applications and establish transferable design principles for developing adaptive meta‐heuristics in complex optimization domains.

ABSTRACT Two of the most essential elements of cloud resource management systems are resource management as well as scheduling. Due to the heterogeneity of resources, their interdependencies, and the unpredictable nature of load in a cloud environment, cloud Resource Scheduling (RS) is the most important issue to handle effectively. One of the most challenging tasks in cloud computing is RS, where resources must be assigned to the necessary tasks or jobs in accordance with the necessary Quality of Service (QoS) of the cloud applications. This study suggests a unique method for scheduling cloud resources based on a virtual data center and machine learning model. The genetic reinforcement kernel swarm optimization methodology and cloud data centers are deployed here. The suggested network analysis aims to balance energy usage and SLA. The suggested framework is assessed by doing experiments on the Google Cluster dataset, Planet Lab, and Bitbrains VM traces, as well as three real‐world workload datasets. Execution cost, Execution time, Makespan, Energy consumption, Resource utilization, and Scalability are factors that were examined here. The proposed framework is superior to several performance indicators compared to state‐of‐the‐art techniques. The project's methodology addresses the issues of load imbalance and excessive migration expenses.

Abstract The comprehensive learning strategy is a meticulous method for enhancing the optimization ability of population‐based optimization algorithms. This article proposes an adaptive artificial electric field algorithm (iAEFA), which is developed by embedding a comprehensive learning strategy into AEFA. The proposed algorithm utilizes a novel adaptive approach for developing a better learning strategy in which an agent's velocity is updated using the comprehensive influence of the entire population. The developed scheme has shown a stronger potential to discover better candidate solutions in each iteration. The objective of the proposed method is to develop an efficient optimizer for continuous optimization problems. The performance of the proposed iAEFA is evaluated using a set of 13 classical benchmark test problems and the CEC 2019 (100‐digit challenge) benchmark functions. The experimental results are compared to seven state‐of‐the‐art optimization algorithms. Using the Wilcoxon signed‐rank test, the statistical significance of the results is confirmed. This article also discusses the theoretical convergence of the proposed algorithm, along with other significant findings about the proposed scheme. The experimental results and the theoretical analysis shows that the proposed scheme can be an excellent choice for the function optimization task compared to other existing algorithms.

Water cycle algorithm (WCA) is a population-based metaheuristic algorithm, inspired by the water cycle process and movement of rivers and streams towards sea. The WCA shows good performance in both exploration and exploitation phases. Further, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of WCA is proved theoretically. In this paper, CEC’15 computationally expensive benchmark problems (i.e., 15 problems) have been considered for efficiency measurement of WCA accompanied with other optimisers. Also, a new discretisation strategy for the WCA has been proposed and applied along with other optimisers for solving combinatorial Internet shopping optimisation problem. By applying complexity analysis, it shows that using the WCA intricacy from dimension 10–30 is increased for almost three times. Proposing a unique discretisation approach along with providing iterative convergence proof can be considered as novelty of this research. By observing the attained numerical results, the WCA could find the minimum average error of CEC’15 in 12 and 8 out of 15 cases for dimensions 10 and 30, respectively. Experimental optimisation results for a wide range computationally expensive problems reveal the effectiveness and advantage of WCA for solving both continuous and discrete optimisation problems.

We have a great pleasure to presenting a special issue of the 4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA 2018) organised by BML Munjal University, Gurugr...

Many real-world and engineering design problems can be formulated as constrained optimisation problems (COPs). Swarm intelligence techniques are a good approach to solve COPs. In this paper an efficient shrinking hypersphere-based particle swarm optimisation (SHPSO) algorithm is proposed for constrained optimisation. The proposed SHPSO is designed in such a way that the movement of the particle is set to move under the influence of shrinking hyperspheres. A parameter-free approach is used to handle the constraints. The performance of the SHPSO is compared against the state-of-the-art algorithms for a set of 24 benchmark problems. An exhaustive comparison of the results is provided statistically as well as graphically. Moreover three engineering design problems namely welded beam design, compressed string design and pressure vessel design problems are solved using SHPSO and the results are compared with the state-of-the-art algorithms.