shuffled frog leaping algorithm

The optimal design and construction of sewage networks have always been considered by researchers and experts due to the very high costs of implementing this infrastructure. Being consisted of various variables and subjected to complex constraints, conventional mathematical optimization procedures are unlikely to be able to solve sewage network optimization problems. Thus, utilizing meta-heuristic optimization algorithms is a must to tackle these problems. The shuffled frog leaping algorithm (SFLA) is one of the new meta-heuristic algorithms which has shown its ability to solve a large number of optimization problems. In this research, the capability of the SFLA in solving the problem of optimal design of sewage networks has been investigated. The diameter of the pipes as discrete decision variables and the depth of pipe placement as continuous decision variables were simultaneously considered in this study as the unknowns. To this end, three sewage networks with 13, 41, and 65 decision variables have been selected as case studies. Various technical, operational, and hydraulic constraints are controlled by defining appropriate penalty functions. The results showed that for case studies 1 and 3, the SFLA decreased the minimum construction costs derived by GA, PSO, and SCE algorithms by 0.43 and 3.2 percent respectively, and for the second case study, with the less number of function evaluations, SFLA has reached the equal objective function compared to other algorithms.

Wireless sensor network (WSN) is a set of spatially distributed sensors with the pre-determined or randomly structure. Coverage, as an important performance indicator of WSNs, is subdivided into three classes of target coverage, area coverage and barrier coverage. This paper investigates problems of target and area coverage for a randomly distributed WSN. To this end, the analytical algorithm of steepest descent (SD) with Armijo and Wolf rules is suggested as a new solution for target coverage, and the SD algorithm along with evolutionary Genetic and Shuffled Frog Leaping algorithms (GA and SFLA) are utilized for the maximization of area coverage. According to the results of performance evaluation over different scenarios, it is confirmed that utilizing SD algorithm for the target coverage could increase the coverage accuracy and reduce the computational complexity compared with the evolutionary method the GA. Moreover, the SD algorithm can manage sensors movement towards targets. Furthermore, in the case of area coverage, the results reveal that SFLA provides more coverage accuracy in comparison with the GA although this improvement leads to more complexity of the SFLA.

Wireless sensor networks consist of thousands of sensor nodes with limited energy. Energy efficiency is a fundamental challenge issue for wireless sensor networks. Clustering sensor nodes in separate categories and exchanging information through clusters is one of the ways to improve energy consumption. This paper presents a new cluster-based routing protocol called SFLCFBA. The proposed protocol biologically uses fast and effective search features inspired by the Shuffled Frog Leaping algorithm, which acts based on the Frog food behavior to cluster sensor nodes. The proposed protocol also uses fuzzy logic to calculate the node fitness, based on the two criteria of distance to the sink and the remaining energy of the sensor node or power of battery level. IEEE 802.15.4 Protocol and NODIC Protocol with the proposed methodology and OPNET Simulator were simulation and the results in terms of energy consumption, end to end delay, signal to noise ratio, the success property data and throughput were compared with each other. The results of the simulation showed that the proposed method outperforms the IEEE 802.15.4 Protocol and NODIC Protocol due to the use of the criteria listed.

Wireless sensor networks (WSN) comprise of a large number of low-power but low-cost small sensing nodes which distributed randomly in a specific area far from the human reach , for the purpose of surveillance, recognition and monitoring the nearby environment based on their inter communication. Each node includes units i.e. sensing, processing, transducing, location positioning and power supply. Owing to various features of sensors such as quickness, self-awareness and self configurability, WSNs have various applications in different areas and many methods are being developed to improve their performance in an application specific way. WSNs face many challenges, including energy restrictions, security, communication reliability, design, and so on. It should be mentioned that it is hardly possible to balance all these challenges due to the conflicts they have with each other. Hitherto, researchers have done extensive studies to bridle these concerns. Sensor nodes are small and have often limited and irreplaceable sources of energy. Furthermore, they can send information at short distances. In long run operations, each node generally does the data collection singly. In this paper, a multi-objective swarm intelligence-based algorithm built on Shuffled frog-leaping and Firefly Algorithm (named MOFSA) is presented as an adaptive clustering-based multi-hop routing protocol for WSNs. MOFSA's multi-objective function regards different criteria (e.g., inter- and intra-cluster distances, residual energy of nodes, distances from the sink, overlap and load of clusters) to select appropriate cluster heads at each round. Moreover, another multi-objective function is proposed to select the forwarder nodes in the routing phase. The controllable parameters of MOFSA in both clustering and multi-hop phases can be adaptively tuned to achieve the best performance based on the network requirements according to the specific application. Simulation outcomes demonstrate average lifetime improvements of 230% compared with LEACH, 100% compared with ERA, 38% compared with SIF and 260% compared with FSFLA in different network scenarios.