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

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.