Ahmed Tchvagha Zeine; Abdelkhalak El Hami; Rachid Ellaia; Emmanuel Pagnacco

In engineering, design problems are generally multi-objective with complex non-linear constraints. Therefore, the computing effort can often rise significantly with the number of objectives and constraints' evaluation. The metaheuristics algorithms are nowadays considered as powerful algorithms to deal with multi-objective optimisation problems. In this article, we develop a new backtracking search algorithm for multi-objective optimisation, called BSAMO, to solve this kind of problems. It is evaluated here through a set of benchmarks problems and two structural design applications. BSAMO's numerical results are compared with those of NSGA-II by two performance measures. They show that the proposed algorithm is able to produce a better convergence towards the Pareto front and to preserve the diversity of the solutions.]]>

Mohamed Abdel-Baset; Haizhou Wu; Yongquan Zhou

In this paper, we apply a complex encoding flower pollination algorithm (CEFPA) to solve constrained engineering optimisation problems. The performance of the proposed algorithm is tested using five standard benchmark functions and four engineering problems. A comparative study of the results with those obtained well-known algorithms is used to validate and verify the efficiency of CEFPA. The CEFPA performs expressively better in terms of the speed and stability for the optimal solution.]]>

Amir T. Payandeh Najafabadi; Ghobad Barmalzan; Shahla Aghaei

Using the Kullback-Leibler (KL) divergence along with Vuong's test, this article constructs a set of appropriate weighted models, say weighted model confidence set, for unknown true density h(·) which is a subset of a class of non-nested models. A weighted confidence set provides an appropriate model for random variable X which some part of its support conveys some important information about the underlying true model. Application of such a weighted model confidence set for local and mixture model confidence sets have been given. Two simulation studies have been conducted to show practical application of our findings.]]>

W.P.T.M. Wickramaarachchi; S.S.N. Perera

Dengue has been a major public health concern in most parts of the tropical world and the dynamics of dengue disease transmission is complex due to several external factors. Various mathematical models have been developed to understand the transmission dynamic of the dengue disease. However, the fixed parameter values have been used in those models so the real dynamics of the transmission is not explained completely. Mosquito density is responsible for the transmission of dengue locally, whilst human mobility causes transmission of the disease globally. Thus mosquito density is a vital parameter for study the dengue transmission which depends heavily on climate, geography and human behaviour. In this study, the density dependent Gompertz model with climate variation factor is used to model the mosquito density. Various levels of climate factor combinations act differently on the dynamics of mosquito density. Thus modelling the climate effect to grow mosquito populations should be done under uncertainty. The fuzzy membership functions are constructed for each factor rainfall and temperature where the membership value in [0, 1] explains the degree of favorability to mosquitoes from each factor in different levels. The Modified Einstein Sum operator is used to compute the overall measure of unfavorability from these two climate factors. The standardised mosquito density and real risk of dengue are compared using urban Colombo data and defining a mapping function. It is noted that 94.77% of data points are able to determine the real dengue risk 90% accurately.]]>

Khadidja Fellah; Bouabdellah Kechar

Recent advances in miniature mobile robotics have fostered the emergence of mobile wireless sensor networks (MWSN). As sensor nodes are battery-powered devices, the first important constraint is how to reduce the energy consumption to extend the lifetime of the network. In this paper, we propose an optimisation approach based on integer linear programming (0-1 ILP) to significantly reduce energy consumption in MWSN. To do so, we formulate the problem as an objective function aiming to minimise the overall energy consumption related to communication operations and the mobility of the sensor nodes and/or sink. The developed formulation is performed under some realistic and relevant constraints of MWSN, such as sensing coverage, connectivity, adjustable transmission range and the case of adjustable sensing range. The proposed approach is evaluated using flat and cluster-based topologies by doing intensive experiments using the CPLEX solver. The obtained results reveal that the mobility factor in MWSN coupled with the considered additional constraints makes it possible to extend significantly the network lifetime.]]>