article · Processes
A new stochastic metaheuristic optimisation method, named the waterwheel plant algorithm, models the hunting behaviour of the waterwheel plant. In this mathematical model, simulated plants act as search agents to locate optimal solutions to complex problems. Testing across twenty-three unimodal and multimodal objective functions demonstrated the algorithm's balance between exploration and exploitation. Performance on unimodal functions confirmed an ability to converge tightly on optimal solutions, while multimodal tests showed effectiveness in scanning broad search spaces to identify primary optimal regions. In addition to theoretical benchmark functions, the method was evaluated on three engineering design challenges. Comparative simulations against established metaheuristic algorithms indicated superior performance, driven by a well-proportioned balance between searching new areas and refining known solutions.
Optimisation problems are common across many scientific and engineering domains. By mimicking natural predatory mechanisms found in waterwheel plants, this algorithm provides a balanced method for navigating complex mathematical landscapes. It enables problem solvers to balance broad exploration of possibilities with precise refinement of promising solutions, improving outcomes in computational design challenges.
The algorithm is aimed at engineering design and scientific problem-solving where complex numerical optimisation is required. Prospective users include software developers, simulation engineers, and computational researchers. Because it has been evaluated solely on mathematical benchmarks and simulated engineering problems rather than deployed within active industrial workflows, the technology remains at an early, algorithmic stage of development.
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Attempting to address optimization problems in various scientific disciplines is a fundamental and significant difficulty requiring optimization. This study presents the waterwheel plant technique (WWPA), a novel stochastic optimization technique motivated by natural systems. The proposed WWPA’s basic concept is based on modeling the waterwheel plant’s natural behavior while on a hunting expedition. To find prey, WWPA uses plants as search agents. We present WWPA’s mathematical model for use in addressing optimization problems. Twenty-three objective functions of varying unimodal and multimodal types were used to assess WWPA’s performance. The results of optimizing unimodal functions demonstrate WWPA’s strong exploitation ability to get close to the optimal solution, while the results of optimizing multimodal functions show WWPA’s strong exploration ability to zero in on the major optimal region of the search space. Three engineering design problems were also used to gauge WWPA’s potential for improving practical programs. The effectiveness of WWPA in optimization was evaluated by comparing its results with those of seven widely used metaheuristic algorithms. When compared with eight competing algorithms, the simulation results and analyses demonstrate that WWPA outperformed them by finding a more proportionate balance between exploration and exploitation.
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DOI: 10.3390/pr11051502
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