Routing is an important challenge in WSN due to the presence of hundreds or thousands of sensor nodes. Low Energy Adaptive Clustering Hierarchy (LEACH) is a hierarchical Routing and data dissemination protocol. LEACH divides a network domain into several sub-domains that are called clusters. Non-uniformity of cluster distribution and cluster heads (CHs) selection without considering the positions of other sensors may reduce the quality of cluster selection. Sensor nodes send data packets over long distances. Imperialist Competitive algorithm (ICA) is an optimization algorithm inspired by social phenomenon. It considers colonization process as a stage of socio-political evolution. We improve performance of the LEACH algorithm using imperialist approach and study its efficacy in terms of energy consumption, coverage and cluster uniformity and compare with those of the LEACH algorithm. Selection of suitable value for radio communication radius over the network lifetime is a trade-off between connectivity and sensors energy consumption. Empowering LEACH with ICA helps to find the best location of a CH in every cluster, conserve energy significantly, increase network lifetime, and maintain network connectivity. It can significantly reduce the number of active sensors going out of range over the lifetime of a network.

The open vehicle Routing problem (OVRP) is a variance of the vehicle Routing problem (VRP) that has a unique character which is its open path form. This means that the vehicles are not required to return to the depot after completing service. Because this problem belongs to the NP-hard problems, many metaheuristic approaches like the ant colony optimization (ACO) have been used to solve OVRP in recent years. The versions of ACO have some shortcomings like its slow computing speed and local-convergence. Therefore, in this paper, we present an efficient hybrid elite ant system called EHEAS in which a new state transition rule, tabu search as an effective local search algorithm and a new pheromone updating rule are used for more improving solutions. These modifications avoid the premature convergence and make better solutions. Computational results on sixteen standard benchmark problem instances show that the proposed algorithm finds closely the best known solutions for most of the instances in which ten best known solutions are also found. In addition, EHEAS is comparable in terms of solution quality to the best performing published metaheuristics.

The vehicle Routing problem with the capacity constraints was considered in this paper. It is quite difficult to achieve an optimal solution with traditional optimization methods by reason of the high computational complexity for large-scale problems. Consequently, new heuristic or metaheuristic approaches have been developed to solve this problem. In this paper, we constructed a new heuristic algorithm based on the tabu search and adaptive large neighborhood search (ALNS) with several specifically designed operators and features to solve the capacitated vehicle Routing problem (CVRP). The effectiveness of the proposed algorithm was illustrated on the benchmark problems.The algorithm provides a better performance on largescaled instances and gained advantage in terms of CPU time.In addition, we solved a real-life CVRP using the proposed algorithm and found the encouraging results by comparison with the current situation that the company is in.

Recently, underwater Wireless Sensor Networks (UWSNs) attracted the interest of many researchers and the past three decades have held the rapid progress of underwater acoustic communication. One of the major problems in UWSNs is how to transfer data from the mobile node to the base stations and choosing the optimized route for data transmission. Secure Routing in UWSNs is necessary for packet delivery. A few researches have been done on secure Routing in UWSNs. In this article, a new secure Routing algorithm called Secure Routing algorithm for Underwater (SRAU) sensor networks is proposed to resist against wormhole and sybil attacks. The results indicate acceptable performance in terms of increasing the packet delivery ratio regarding the wormhole and sybil attacks, increasing network lifetime through balancing the network energy consumption, high detection rates against the attacks, and decreasing the end to end delay.

A significant portion of Gross Domestic Production (GDP) in any country belongs to the transportation system. Transportation equipment, is supposed to be great consumer of oil products. In this papera novel heuristic algorithmbased on Clark and Wright algorithm called Green Clark and Wright (GCW) for Green Vehicle Routing Problem is presented. The objective function is fuel consumption, drivers, and the usage of vehicles. Comparing obtained results by those of exact methods solutions for small-sized problems and Differential Evolution (DE) algorithm solutions for large-scaled problems, the results show efficient performance of the proposed GCW algorithm.

Vehicular Ad hoc NETworks (VANETs) are a particular type of Mobile Ad hoc NETworks (MANETs) in which the vehicles are considered as nodes. Due to the rapid topology change and frequent disconnection in these networks, it is difficult to design an efficient Routing protocol for Routing data among vehicles. In this work, a new Routing protocol is provided based on the glowworm swarm optimization algorithm. By using the glowworm algorithm, the proposed protocol detects the optimal route between three-way and intersections. Then the packets are delivered based on the selected routes. The proposed algorithm assigns a value to each route from a source to the destination using the glowworm swarm optimization algorithm, which is a distributed heuristic algorithm. Then a route with a higher value is selected to send messages from the source to the destination. The simulation results show that the proposed algorithm has a better performance than the similar algorithms.