In wireless sensor networks, the network lifespan is a key factor in evaluating the effectiveness of a routing protocol. Most traditional routing protocols optimize cluster head election and intercluster routing as se...
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In wireless sensor networks, the network lifespan is a key factor in evaluating the effectiveness of a routing protocol. Most traditional routing protocols optimize cluster head election and intercluster routing as separate phases, which limits improvements in intercluster routing optimization. To address this issue, we propose a multi-hop routing protocol based on the Spider Wasp Optimizer (swo) algorithm. This protocol integrates cluster head election and intercluster routing in the cluster head selection phase, using the swo algorithm for optimization. Many multi-hop routing protocols select the shortest total path for data transmission between clusters. However, this approach can result in the distance between two cluster heads exceeding a predefined threshold, leading to increased energy consumption during transmission. To mitigate this, we introduce a communication distance factor in the objective function for optimization, employing intermediate relay points to avoid long-distance transmissions. Specifically, we propose a central relay point strategy to minimize forwarding energy consumption. To address the issue of forwarding load optimization, we utilize K-means clustering to group cluster heads and combine this with equidistant relay points and the Dijkstra algorithm to identify the optimal multi-hop paths between clusters, thereby extending the network's lifespan. The proposed routing algorithm is implemented in MATLAB and compared with the PSO-C, EECHS-ISSADE, HBACS, and SSA-FND protocols. In the simulated scenarios, the network lifespan was improved by up to 32.7%, 27.5%, 18.2%, and 9.2%, respectively.
It is very important to jointly determine effective berth and quay crane allocation plan in order to make full use of the limited berth and quay crane resource and improve container terminal transportation efficiency....
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It is very important to jointly determine effective berth and quay crane allocation plan in order to make full use of the limited berth and quay crane resource and improve container terminal transportation efficiency. The uncertainty of ship's arrival time and the quantity of loading and unloading, however, will disrupt the execution of the berth and quay crane allocation plan and increase the cost. In order to minimize the total cost of time and position deviation, the optimal model of berth and quay crane allocation is proposed. The combination of swo algorithm and PGA algorithm is presented to solve the optimization model, because swo algorithm can effectively reduce the dimension of the solution and can avoid the PGA algorithm to converge to the local optimal solution. The correctness of the model and the validity of the algorithm are verified by numerical analysis.
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