Elite artificial bees' colony algorithm to solve robot's fuzzy constrained routing problem

Some New Symbolic Algorithms for the Computation of Generalized Asymptotes

We present symbolic algorithms for computing the g-asymptotes, or generalized asymptotes, of a plane algebraic curve, C, implicitly or parametrically defined. The g-asymptotes generalize the classical concept of asymptotes of a plane algebraic curve. Both notions have been previously studied for analyzing the geometry and topology of a curve at infinity points, as well as to detect the symmetries that can occur in coordinates far from the origin. Thus, based on this research, and in order to solve practical problems in the fields of science and engineering, we present the pseudocodes and implementations of algorithms based on the Puiseux series expansion to construct the g-asymptotes of a plane algebraic curve, implicitly or parametrically defined. Additionally, we propose some new symbolic methods and their corresponding implementations which improve the efficiency of the preceding. These new methods are based on the computation of limits and derivatives; they show higher computational performance, demanding fewer hardware resources and system requirements, as well as reducing computer overload. Finally, as a novelty in this research area, a comparative analysis for all the algorithms is carried out, considering the properties of the input curves and their outcomes, to analyze their efficiency and to establish comparative criteria between them.

BTA-MM: Burst traffic awareness-based adaptive mobility model with mobile sinks for heterogeneous wireless sensor networks

Using mobile sink increases the coverage time and energy expenditure when there is a burst traffic condition in wireless sensor networks (WSNs). Hence, it is essential to handle burst traffic using mobile sinks in an energy-efficient mobility manner. In most studies, mechanisms of the clustering and routing have not taken into account burst traffic. In fact, the number of studies with integrated mobile sink nodes, burst traffic awareness, multi-criteria cluster head (CH) selection, and mobile sink routing is negligible. For this purpose, a novel burst traffic awareness adaptive mobility scheme is proposed based on heterogeneous clustered WSNs, namely Burst Traffic Awareness-Mobility Model (BTA-MM). In the proposed scheme, the network area is first divided into two cluster groups. The CH selection is performed for each round by the average residual energy an d node load, taking into account the network coverage. An adaptive Gauss-Markov-burst traffic combination model is proposed in the study. In the proposed model, the mobile sinks collect all data in a single-hop communication as soon as they join the coverage intersection points (CIPs) of the CHs. The mobility model utilizes the adaptive minimum-weighted cost of the nodes. Once the burst packets are perceived in a CH, the data packets are backed up on the nearest and highest energy node in the cluster. Then, the mobile sink suddenly updates its trajectory towards the node, including the burst data traffic. Performance analysis of the proposed scheme was performed in NS-2 simulation environment. The most notable of the performance results is that the proposed method increased the network lifetime 42.5% more than any other method and also reduced the control overhead and average mobile path length by 72.5% and 35.9%, respectively. Also, the simulation results showed that the proposed method significantly reduced the average energy consumption 34.2% more than any other method, and increased the packet delivery rate 4.5% more than any other method, even in burst traffic.

A novel approach for the next software release using a binary artificial algae algorithm

In software incremental development methodology, the product develops in several releases. In each release, one set of the requirements is suggested for development. The development team must select a subset of the proposed requirements for development in the next release such that by consideration the limitation of the problem provides the highest satisfaction to the customers and the lowest cost to the company. This problem is known as the next release problem. In complex projects where the number of requirements is high, development teams cannot choose an optimized subset of the requirements by traditional methods, so an intelligent algorithm is required to help in the decision-making process. The main contributions of this study are fivefold: (1) The customer satisfaction and the cost of every requirement are determined by use of fuzzy numbers because of the possible changing of the customers’ priorities during the product development period; (2) An improved approximate approach is suggested for summing fuzzy numbers of different kinds, (3) A new metaheuristic algorithm namely the Binary Artificial Algae Algorithm is used for choosing an optimized subset of requirements, (4) Experiments performed on two fuzzy datasets confirm that the resulted subsets from the suggested algorithm are free of human mistake and can be a great guidance to development teams in making decisions.

Modified artificial bee colony algorithm for solving mixed interval-valued fuzzy shortest path problem

In recent years, numerous researchers examined and analyzed several different types of uncertainty in shortest path (SP) problems. However, those SP problems in which the costs of arcs are expressed in terms of mixed interval-valued fuzzy numbers are less addressed. Here, for solving such uncertain SP problems, first a new procedure is extended to approximate the summation of mixed interval-valued fuzzy numbers using alpha cuts. Then, an extended distance function is introduced for comparing the path weights. Finally, we intend to use a modified artificial bee colony (MABC) algorithm to find the interval-valued membership function of SP in such mixed interval-valued fuzzy network. The proposed algorithm is illustrated via two applications of SP problems in wireless sensor networks and then the results are compared with those derived from genetic and particle swarm optimization (PSO) algorithms, based on three indexes convergence iteration, convergence time and run time. The obtained results confirm that the MABC algorithm has less convergence iteration, convergence time and implementation time compared to GA and PSO algorithm.

Solving fuzzy multi-objective shortest path problem based on data envelopment analysis approach

The shortest path problem (SPP) is a special network structured linear programming problem that appears in a wide range of applications. Classical SPPs consider only one objective in the networks while some or all of the multiple, conflicting and incommensurate objectives such as optimization of cost, profit, time, distance, risk, and quality of service may arise together in real-world applications. These types of SPPs are known as the multi-objective shortest path problem (MOSPP) and can be solved with the existing various approaches. This paper develops a Data Envelopment Analysis (DEA)-based approach to solve the MOSPP with fuzzy parameters (FMOSPP) to account for real situations where input–output data include uncertainty of triangular membership form. This approach to make a connection between the MOSPP and DEA is more flexible to deal with real practical applications. To this end, each arc in a FMOSPP is considered as a decision-making unit with multiple fuzzy inputs and outputs. Then two fuzzy efficiency scores are obtained corresponding to each arc. These fuzzy efficiency scores are combined to define a unique fuzzy relative efficiency. Hence, the FMOSPP is converted into a single objective Fuzzy Shortest Path Problem (FSPP) that can be solved using existing FSPP algorithms.