In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA) for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. However, during the process of chemotaxis, the BFOA depends on random search directions which may lead to delay in reaching the global solution. Recently, a new algorithm BFOA oriented by PSO termed BF-PSO has shown superior in proportional integral derivative controller tuning application. In order to examine the global search capability of BF-PSO, we evaluate the performance of BFOA and BF-PSO on 23 numerical benchmark functions. In BF-PSO, the search directions of tumble behavior for each bacterium oriented by the individual's best location and the global best location. The experimental results show that BF-PSO performs much better than BFOA for almost all test functions. That's approved that the BFOA oriented by PSO strategy improve its global optimization capability.
Emergency events such as natural disasters, environmental events, sudden illness, and social security events pose tremendous threats to people’s lives and property security. In order to meet emergency service demands by rationally allocating mobile facilities, an emergency mobile facility routing model is proposed to maximize the total served demand by the available mobile facilities. Based on the uninterruptible feature of emergency services, the model abstracts emergency events act as a combination of multiple uncertain variables. To overcome the computational difficulty, a robust optimization approach and genetic algorithm are employed to obtain solutions. Illustrative examples show that it provides an effective method for solving the emergency mobile facility routing problem, and that the risk factor and penalty factor of the model can further guide decision-making.
Abstract-The Vehicle Routing Problem (VRP) is a NPcomplete problem and is also a hot topic in the operational research. But traditional methods might suffer from slow convergence and the curse of large sizes, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO) is known to effectively solve engineering optimization problems. In this paper, the PSO in solving VRP is comprehensive surveyed from two basic aspects: the improved PSO algorithms and the particle encoding method. For each application, technical details that are required are all discussed. Finally, a summary is given together with suggestions for future research.
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