Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem

Abstract

A wireless sensor network (WSN) is a distributed network system composed of a great many sensor nodes that rely on self-organization. The random deployment of WSNs in city planning often leads to the problem of low coverage of monitoring areas. In the construction of smart cities in particular, a large number of sensor nodes need to be deployed to maintain the reception, processing, and transmission of data throughout the city. However, the uneven distribution of nodes can cause a lot of wasted resources. To solve this problem, this paper proposes a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The algorithm introduces a dynamic inertia weight adjustment strategy in the global exploration and local exploitation stages of the standard marine predator algorithm to balance the exploration and exploitation capabilities of the algorithm and improve its solution accuracy. At the same time, the improved algorithm uses a multi-elite random leading strategy to enhance the information exchange rate between population individuals and improve the algorithm’s ability to jump out of the local optimum. The optimization experiment results of 11 benchmark test functions and part of the CEC2014 test functions show that the optimization performance of the improved algorithm is significantly better than the standard marine predator algorithm and other algorithms in the literature. Finally, the improved algorithm is applied to the WSN coverage optimization problem. The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms and other improved algorithms in the literature for solving the WSN coverage optimization problem.

An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

“…Since the inception of MPA, an extensive series of MPA's variants have been developed and further introduced in the Table 3 Abbreviations of revised MPA variants along with its full form for algorithms projected in Table 2 S. No Revised MPA variants Abbreviation Improved Marine Predator Algorithm-I [147] IMPA-I Improved Marine Predator Algorithm-II [148] IMPA-II Improved Marine Predator Algorithm-III [167] IMPA-III Improved Marine Predators' Optimization Algorithm [148] IMPOA Improved Modified Marine Predators Algorithm [181] IMMPA Modified Marine Predator Algorithm [161] MMPA-I Modified Marine Predator Algorithm [169] MMPA-II Modified Marine Predator Optimizer [151] MMPO Modified Self Adaptive Marine Predators Algorithm [150] MMPA-SA Binary Marine Predator Algorithm using Time-Varying Sine and V-shaped transfer functions [149] BMPA-TVSinV Binary Marine Predator Algorithm [168] BMPA Multi-Objective Marine Predator Algorithm-I [170] MOMPA-I Multi-Objective Marine Predator Algorithm-II [171] MOMPA-II Multi-Objective Marine Predator Algorithm-III [175] MOMPA-III Multi-Objective Enhanced Marine Predators Algorithm [140] MOEMPA Enhanced Marine Predator Algorithm [172] EMPA Enhanced Marine Predator Algorithm with Local Escaping Operator [173] LEO-EMPA Multi-Strategy Marine Predator Algorithm [174] MSMPA-JRSSELM Multi-Strategy Marine Predator Algorithm (Neighborhood) [160] N-MPA Multi-Group Marine Predator Algorithm [176] MGMPA Quantum Marine Predator Algorithm [178] QMPA Advanced Marine Predator Algorithm [146] AMPA Fuzzy Marine Predator Algorithm [145] FMPA Chaos Embed Marine Predator Algorithm [141] CMMPA Stochastic Marine Predator Algorithm with Multiple Candidates [144] SMPA-MC Fusion Multi-Strategy Marine Predator Algorithm [142] FMMPA Fractional Order Marine Predators Algorithm [180] FO-MPA Co-Evolutionary Cultural mechanism-based Marine Predators Algorithm…”

A Multi-Strategy Improved Sparrow Search Algorithm for Coverage Optimization in a WSN

“…However, the effect of the algorithm is unstable. He et al [21] proposed a WSN coverage optimization model based on an improved marine predator algorithm (IMPA). The simulation results demonstrate that the IMPA has a better coverage rate than other metaheuristic algorithms.…”

On Performance of Marine Predators Algorithm in Training of Feed-Forward Neural Network for Identification of Nonlinear Systems

“…Abdel-Basset et al [26] developed a new binary MPA (BMPA) for the 0-1 knapsack problem. Apart from these, there are different MPA-based studies in the literature [27][28][29][30][31].…”