Optimization of Route Repair in Mobile Ad-hoc Network

A main shortcoming of mobile Ad-hoc network's reactive routing protocols is the large volume of far-reaching control traffic required to support the route discovery (RD) and route repair (RR) mechanism. Using a random mobility model, this paper derives the probability equation of the relative distance (RDIS) between any two mobile hosts in an ad-hoc network. Consequently, combining with average equivalent hop distance (AEHD), a host can estimate the routing hops between itself and any destination host each time the RD/RR procedure is triggered, and reduce the flooding area of RD/RR messages. Simulation results show that this optimized route repair (ORR) algorithm can significantly decrease the communication overhead of RR process by about 35%.

Risk Based Optimal Inspection and Repair Planning for Ship Structures Subjected to Corrosion Deterioration

A framework of risk based inspection and repair planning was presented to optimize for the ship structures subjected to corrosion deterioration. The planning problem was formulated as an optimization problem where the expected lifetime costs were minimized with a constraint on the minimum acceptable reliability index. The safety margins were established for the inspection events, the repair events and the failure events for ship structures. Moreover, the formulae were derived to calculate failure probabilities and repair probabilities. Based on them, a component subjected to corrosion is investigated for illustration of the process of selecting the optimal inspection and repair strategy. Furthermore, some sensitivity studies were provided. The results show that the optimal inspection instants should take place before the reliability index reaches the minimum acceptable reliability index. The optimal target failure probability is 10 -3 . In addition, a balance can be achieved between the risk cost and total expected inspection and repair costs by means of the risk-based optimal inspection and repair method, which is very effective in selecting the optimal inspection and repair strategy.

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.