Sailfish optimizer algorithm (SFO) for optimized clustering in wireless sensor network (WSN)

Abstract

Purpose Inspired optimization algorithms respond to numerous scientific and engineering difficulties based on its flexibility and simplicity. Such algorithms are valid for optimization difficulties devoid of structural alterations. Design/methodology/approach This paper presents a nature-inspired optimization algorithm, named Sailfish optimizer (SFO) stimulated using sailfish group. Monetary custom of energy is a dangerous problem on wireless sensor network (WSN). Findings Network cluster is an effective method of reducing node power consumption and increasing network life. An algorithm for selecting cluster head (CHs) based on enhanced cuckoo search was proposed. But this algorithm uses a novel encoding system and wellness work. It integrates a few problems. To overthrow this method many metaheuristic-based CH selection algorithms are presented. To avoid this problem, this paper proposed the SFO algorithm based energy-efficient CH selection of WSN. Originality/value The proposed SFO algorithm based energy-efficient algorithm is used for discovering the CHs ideal situation. The simulations under delay, delratio, drop, energy, network lifetime, overhead and throughput are carried out.

Hybrid Red Fox and Improved Whale Optimization Algorithm-based Clustering scheme for improving network longevity in Wireless Sensor Networks

“…The results of this DSOBCA confirmed its predominance in improving the residual energy, network throughput and dead nodes per round. Kumar et al [19] contributed an optimized clustering scheme using Sailfish optimizer algorithm (SFOACS) for selecting potential sensor nodes as CH with minimized time complexity. It targeted on identifying the ideal situations in which CH selection can be achieved by preventing energy holes in the network.…”

“…Step 23: Modify the locations of the sub-population 'X C ' using Equations (17)(18)(19) Step 24: End If…”

Energy balance algorithm for heterogeneous wireless sensors based on bilevel decision-making and data distribution

“…In addition, competition mechanism is introduced to optimize the selection of cluster heads and make the cluster heads evenly distributed [7]. In the data transmission stage, A multi-objective decision-making strategy is used to synthesize multiple factors for data distribution [8]. Finally, through simulation results, the routing algorithm has a great improvement in the network life cycle, energy utilization rate and data transmission.…”