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In this article, we present two resilient algorithms, the improved alternative hard c‐means (IAHCM) and the improved alternative fuzzy c‐means (IAFCM). We implement the Gaussian distance‐dependent function proposed by Zhang and Chen (D.‐Q. Zhang and Chen, 2004). In some cases, Zhang and Chen's metric distance does not account for the clustering centroid effect predicted by the large value. R* is employed in IAHCM and IAFCM to discover robust results while minimizing its sensitivity. Experiments are conducted using two‐and three‐dimensional data, including Diamond and Iris real‐world data. The results are based on demonstrating the robust simplicity and applicability of the offered algorithms. Similarly, computational complexity is assessed. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are capable of achieving data dissemination between them such that exploration of their potential could be performed based on their frequency range. It is considered to be highly difficult for recharging sensor devices under adverse situations. The main drawbacks of WSNs concern to the issue of network lifetime, coverage area, scheduling and data aggregation. In particular, prolonging network lifetime confirms the success together with the energy conservation of sensor nodes, data transmission reliability and scalability of their operation in data aggregation. Clustering schemes are considered to be highly suitable for effectively utilising the resources with lower overhead, such that energy consumption is enhanced for upgrading the network lifespan. In this paper, a Hybrid Modified Artificial Bee Colony and Firefly Algorithm (HMABCFA) -Based Cluster Head Selection is proposed for ensuring energy stabilisation, delay minimisation and inter-node distance reduction for improving the network lifetime. This proposed HMABCFA integrates the benefit of the Firefly optimisation algorithm for generating a new position that which has the capability of replacing the position, which is not updated in the scout bee phase of ABC. This incorporation of Firefly optimisation algorithm into the ABC algorithm prevents the limitations of premature convergence, slow convergence and the possibility of being trapped into the local point of optimality in the clustering process. The modified ABC-based clustering process is phenomenal in improving the feasible dimensions for enhancing the process of exploitation and exploration. The results of the HMABCFA, on an average are confirmed to enhance the network lifetime by 23.21%, energy stability by 19.84% and reduce network latency by 22.88%, compared to the benchmarked approaches. [ABSTRACT FROM AUTHOR]

The weapon target assignment (WTA) problem is an important task to tactical arrangements in military commitment operations. It describes the optimal method to allocate defense in opposition to threats in fighting situations. It is an NP-complete issue in which no accurate outcome for all conceivable situations is known. The time performance of created algorithms is a major challenge in modeling the WTA problem, which has only been lately considered in related papers. This article improves the recently developed algorithm called improved Tunicate Swarm Algorithm (iTSA) which is inspired by the natural behavior of tunicates to solve the WTA problem. The suggested method is compared with well-known metaheuristic approaches. The experimental findings show that the method presented works better than previous competing metaheuristic approaches. [ABSTRACT FROM AUTHOR]

Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users' requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%. [ABSTRACT FROM AUTHOR]

The article titled "Retraction Note: iTSA: An improved tunicate Swarm Algorithm for defensive resource assignment problem" has been retracted by the publisher due to concerns regarding compromised editorial handling and peer review processes. The authors listed in the retracted article are Gaurav Dhiman, Kusum Yadav, Jalawi Sulaiman Alshudukhi, and Wattana Viriyasitavat, affiliated with institutions in Saudi Arabia, India, and Thailand. The retraction note emphasizes that the publisher no longer has confidence in the results and conclusions of the article, and none of the authors have responded to correspondence regarding the retraction. [Extracted from the article]