@article{MAKHILLJEAS2019141818413,
    title = {Mathematical Function and Algorithms Optimisation for Wireless Sensor Networks},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {18},
    pages = {6668-6674},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.6668.6674},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.6668.6674},
    author = {Ali,Ong Bi and},
    keywords = {Configurations,searching optimisation,WSND,(LMOJPSO),problem necessitate,NSGA-II},
    abstract = {Wireless Sensor Network Deployment or (WSND) is considered as an active research subject. Its goal
is to plan the sensor network’s configurations to achieve maximum coverage and lifetime while incurring
minimum cost. Meta-heuristic searching optimisation was utilised to solve this problem. However, the complex
optimisation surface and the multi-objective characteristic of this problem necessitate the development of
customisable multi-objective meta-heuristic searching optimisation. For this study, Lagged Multi-Objective
Jumping Particle Swarm Optimisation (LMOJPSO) was formulated to solve WSND. LMOJPSO is considered as
a new multi-objective optimisation for WSND. Conduct of the optimisation search took place by utilising three
kinds of Pareto front: iteration, global and local. Furthermore, the lag is incorporated in the algorithm for
iteration of the Pareto front. From the MOO perspective this offers better Pareto solutions. Results of its
comparison with state-of-the-art approach NSGA-II reveals that LMOJPSO is better compared to it.}
    }