Abstract
Disassembly sequence planning (DSP) is an important part of equipment maintenance in a hydropower station. In this paper, the generation of an excellent disassembly sequence (DS) for equipment is studied. Firstly, according to the characteristics of hydropower equipment, a combination node type is added to the directed graph analysis model, and the distance factor of components in space is added to the evaluation function of DS. Secondly, a DSP strategy including the grouping and minimization of the node's scope is adopted to reduce computational complexity. Thirdly, a novel team-based genetic algorithm (TBGA) combining teams, fast feasible solution generator (FFSG), precedence preservative crossover (PPX) mechanism, multi-point heuristic mutation (MHM) mechanism, and forward-and-backward optimization operator (FBOO) is designed for DSP. The proposed TBGA maintains global search capabilities through teams and enhances local search capabilities through individuals. In the evolutionary process, teams, MHM, and FBOO have good complementarity to improve the comprehensive performance of the algorithm. Finally, four experiments are conducted and the performance of TBGA is tested based on the comparison of a well-known genetic algorithm, simplified teaching-learning-based optimization, and simplified swarm optimization algorithm. The results show that the proposed method can get better search results in limited iterations and require only about 25% time of other algorithms.
Highlights
As an important part of the power grid, hydropower station has played a key role in peaking and frequency modulation
To make up for this deficiency, this paper presents a team-based genetic algorithm (TBGA)
This article analyzes the characteristics of HES and uses the improved directed constraint graph to build the relationship between components
Summary
As an important part of the power grid, hydropower station has played a key role in peaking and frequency modulation. If the mutation mechanism is changed and directed to a better disassembly sequence, the search efficiency can be optimized For this purpose, a multi-point heuristic mutation (MHM) mechanism based on heuristic function is adopted. Step 4: Calculate the heuristic function H (m) of all the mutation and record the inserted position and its H (m) value; Step 5: Sort the value of the H (m) generated in step 4, and the new sequence with the maximal H (m) is the result; Step 6: Repeat Step2-Step NM times to complete the mutation between two generations of a population This mutation mechanism allows elite individuals to mutate in a less costly direction guided by heuristic function. All experiments implemented hereinafter are coded in Java and carried out on the Intel Core i7-4600 2.1-GHz∗4 PC with 8-GB memory
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