Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum...
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Effective tower crane layout planning is essential for the success of construction projects. Traditional optimization algorithms, which often provide a single optimal solution, may not always reveal the global optimum, leaving room for doubt. This paper introduces the competitor algorithm, a novel multi-optimal solution approach inspired by the competitive learning paradigm within classroom settings. This algorithm is designed to provide users with a diverse set of competitive solutions, while avoiding falling into local optima. This strategic diversification ensures that users are equipped with a comprehensive range of options, empowering them to make confident, informed decisions. Furthermore, we have streamlined the positioning range for tower cranes, transitioning from a two-dimensional plane to a one-dimensional segmented line, thus eliminating the need to explore extensive, non-competitive regions. The competitor algorithm's performance was validated through practical application, showcasing both its stability and optimization prowess, thereby confirming its reliable utility in real-world scenarios.
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