article · Knowledge-Based Systems
A new optimization method called the Hiking Optimization Algorithm models search behaviour on the mechanics of human hiking. The approach uses Tobler Hiking Function, which calculates walking velocity based on terrain elevation and distance covered, to guide search agents through complex mathematical landscapes. Evaluation across twenty-nine standard benchmark functions, three structural engineering design problems, and two complex computational challenges shows strong performance. Specifically, the technique was tested on I-beam design, tension or compression springs, gear trains, the travelling salesman problem, and the knapsack problem. Statistical tests comparing it against fourteen established and emerging optimization algorithms indicate that the approach produces competitive results and frequently outperforms existing techniques.
Complex engineering and logistical tasks often require finding the best possible outcome from millions of possibilities. By mimicking how humans navigate varied terrain, this algorithm provides an effective new mathematical tool for solving difficult optimization challenges in design and resource allocation.
The method addresses foundational optimization tasks with direct relevance to mechanical design, structural engineering, and logistics planning, as demonstrated by tests on gear trains and routing problems. However, the work represents early-stage algorithmic research tested on standard benchmark models rather than live commercial systems, meaning practical deployment will require integration into domain-specific software tools.
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In this paper, a novel metaheuristic called ‘The Hiking Optimization Algorithm’ (HOA) is proposed. HOA is inspired by hiking, a popular recreational activity, in recognition of the similarity between the search landscapes of optimization problems and the mountainous terrains traversed by hikers. HOA’s mathematical model is premised on Tobler’s Hiking Function (THF), which determines the walking velocity of hikers (i.e. agents) by considering the elevation of the terrain and the distance covered. THF is employed in determining hikers’ positions in the course of solving an optimization problem. HOA’s performance is demonstrated by benchmarking with 29 well-known test functions (including unimodal, multimodal, fixed-dimension multimodal, and composite functions), three engineering design problems (EDPs), (including I-beam, tension/compression spring, and gear train problems) and two N-P Hard problems (i.e. Traveling Salesman’s and Knapsack Problems). Moreover, HOA’s results are verified by comparison to 14 other metaheuristics, including Teaching Learning Based Optimization (TLBO), Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization, Grey Wolf Optimizer (GWO) as well as newly introduced algorithms such as Komodo Mlipir Algorithm (KMA), Quadratic Interpolation Optimization (QIO), and Coronavirus Optimization Algorithm (COVIDOA). In this study, we employ statistical tests such as the Wilcoxon rank sum, Friedman test, and Dunn’s post hoc test for the performance evaluation. HOA’s results are competitive and, in many instances, outperform the aforementioned well-known metaheuristics.
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DOI: 10.1016/j.knosys.2024.111880
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