Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determin...
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Drinking water safety is a safety issue that the whole society attaches great importance to currently. For sudden water pollution accidents, it is necessary to trace the water pollution source in real time to determine the pollution source's characteristic information and provide technical support to emergency management departments for decision making. The problems of water pollution's real-time traceability are as follows: non-uniqueness and dynamic real time of pollution sources. Aiming at these two difficulties, an intelligent traceability algorithm based on dynamic multi-mode optimization was designed and proposed in the work. As a multi-mode optimization problem, pollution traceability could have multiple similar optimal solutions. Firstly, the new algorithm divided the population reasonably through the optimal subpopulation division strategy, which made the nodes' distribution in a single subpopulation more similar and conducive to local optimization. Then, a similar peak penalty strategy was used to eliminate similar solutions and reduce the non-unique solutions' number, since real-time traceability required higher algorithm convergence than traditional offline traceability and dynamic problems with parameter changes, historical information preservation, and adaptive initialization strategies could make reasonable use of the algorithm's historical knowledge to improve the population space and increase the population convergence rate when the problem changed. The experimental results showed the proposed new algorithm's effectiveness in solving problems-accurately tracing the source of pollution, and obtain corresponding characteristic information in a short time.
In multi-mode heuristic optimization, the output fitness of an algorithm cannot converge to the global optimal value if its search points have not converged to the region with optimal solution. Generally, more samplin...
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ISBN:
(纸本)9789819947546;9789819947553
In multi-mode heuristic optimization, the output fitness of an algorithm cannot converge to the global optimal value if its search points have not converged to the region with optimal solution. Generally, more samplings and more converged points in this optimal region may result in a higher probability of fitness convergence toward the optimal value. However, studies focus mainly on fitness convergence rather than region convergence (RC) of search points. This is partly because, for most objective functions, it is usually hard to track the region of search points in dynamic optimization. To remedy this, a novel analysis method is proposed using the double-well function (DWF), since it has a unique fitness landscape that makes it convenient to trace these points. First, a mathematical analysis of the DWF is given to explore its landscape. Then, RC is defined and discussed using DWF. On these bases, experiments are conducted and analyzed using Particle Swarm optimization (PSO), and much useful information about its RC is revealed. Besides, this method can be used to analyze the RC of similar optimization algorithms as well.
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