A novel chaotic transient search optimization algorithm for global optimization, real-world engineering problems and feature selection

Abstract

Metaheuristic optimization algorithms manage the search process to explore search domains efficiently and are used efficiently in large-scale, complex problems. Transient Search Algorithm (TSO) is a recently proposed physics-based metaheuristic method inspired by the transient behavior of switched electrical circuits containing storage elements such as inductance and capacitance. TSO is still a new metaheuristic method; it tends to get stuck with local optimal solutions and offers solutions with low precision and a sluggish convergence rate. In order to improve the performance of metaheuristic methods, different approaches can be integrated and methods can be hybridized to achieve faster convergence with high accuracy by balancing the exploitation and exploration stages. Chaotic maps are effectively used to improve the performance of metaheuristic methods by escaping the local optimum and increasing the convergence rate. In this study, chaotic maps are included in the TSO search process to improve performance and accelerate global convergence. In order to prevent the slow convergence rate and the classical TSO algorithm from getting stuck in local solutions, 10 different chaotic maps that generate chaotic values instead of random values in TSO processes are proposed for the first time. Thus, ergodicity and non-repeatability are improved, and convergence speed and accuracy are increased. The performance of Chaotic Transient Search Algorithm (CTSO) in global optimization was investigated using the IEEE Congress on Evolutionary Computation (CEC)’17 benchmarking functions. Its performance in real-world engineering problems was investigated for speed reducer, tension compression spring, welded beam design, pressure vessel, and three-bar truss design problems. In addition, the performance of CTSO as a feature selection method was evaluated on 10 different University of California, Irvine (UCI) standard datasets. The results of the simulation showed that Gaussian and Sinusoidal maps in most of the comparison functions, Sinusoidal map in most of the real-world engineering problems, and finally the generally proposed CTSOs in feature selection outperform standard TSO and other competitive metaheuristic methods. Real application results demonstrate that the suggested approach is more effective than standard TSO.

Maize seeds forecasting with hybrid directional and bi‐directional long short‐term memory models

“…The structure of this layer is the same as the neural network structure. In this layer, classification processes are performed as learning the image features by the network (Altay & Altay, 2023a ; Unal et al., 2022 ).…”

Multi-strategy synthetized equilibrium optimizer and application

“…Inspired by natural phenomena and biological behavior ( Ghasemi et al, 2023a ), researchers have proposed many meta-heuristic algorithms (MAs) to solve OPs better. They include genetic algorithm (GA) ( Zhou et al, 2021 ), simulated annealing (SA) ( Kirkpatrick, Gelatt & Vecchi, 1983 ), particle swarm optimization (PSO) ( Chen & Lin, 2009 ), differential evolution (DE) ( Mohamed, Hadi & Jambi, 2019 ), Shuffled Frog Leaping algorithm (SFLA) ( Houssein et al, 2021 ), Artificial Bee Colony (ABC) ( Altay & Varol Altay, 2023 ), biogeography-based optimization (BBO) ( Simon, 2008 ), Cuckoo Search (CS) ( Gandomi, Yang & Alavi, 2013 ), Grey Wolf Optimizer (GWO) ( Mirjalili, Mirjalili & Lewis, 2014 ), etc . MAs are applied in many fields, such as feature selection ( Ghasemi et al, 2023b ), economic dispatch ( Ayedi, 2023 ) due to their simple structure, easy application, and no derivative information on OPs.…”