Abstract
An efficient metaheuristic optimization method called Jaya Algorithm (JA) has gained wide acceptance among optimization researchers in various engineering problems recently. The main feature of JA is that it does not use algorithm-specific parameters and has a very simple formulation based on the concept of approaching the best solution and moving away from the worst solution. This study presents the JA formulation for design optimization of planar steel frames under strength and displacement constraints. The validity of JA is investigated by solving two benchmark design examples. The results demonstrated the superiority of JA over other state-of-the-art metaheuristic optimization methods in terms of optimized weight, number of structural analyses and several statistical parameters.
Highlights
The metaheuristic optimization methods that mimic natural phenomena has been implemented for solving different design problems over the past three decades
The standard Jaya Algorithm (JA) was modified in order to improve its performance
Two planar steel frames previously optimized by various metaheuristic optimization methods are designed to demonstrate the validity of JA
Summary
The metaheuristic optimization methods that mimic natural phenomena has been implemented for solving different design problems over the past three decades. A metaheuristic could be defined as the process of an iterative generation which sheds light on a heuristic by incorporating smartly different concepts for exploration and exploitation of the search space and achieving strategies in order to find near-optimum solutions [1]. Exploration and exploitation are the most significant concepts of finding the best solution in all metaheuristic optimization methods. Exploration provides generating diverse solutions in order to explore search space on a global scale whereas exploitation focuses on the search in a local region by exploiting the information. The balance between exploration and exploitation allows to identify regions containing high-quality solutions and move away from previously explored regions that are far from global optimum. In the last two decades, the bio-inspired approaches (Genetic algorithm (GA) [2], Particle swarm (PSO) [3], Ant colony (ACO) [4], Honey bee mating (HBMO) [5], Enhanced honey bee mating (EHBMO) [6], Whale optimization algorithm (WOA) [7], Enhanced whale optimization algorithm (EWOA) [8] etc.) and physicinspired approaches (Simulating annealing (SA) [9], Harmony search (HS) [10], Big-bang big-crunch [11], Colliding bodies (CBO) [12] etc.) have been proposed for the optimization problems and extended by enhancing their capabilities in optimization procedures such as the convergence, time consumption and achieving the nearglobal optima
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