Revolutionizing Vehicle Cruise Control: An Elite Opposition-Based Pattern Search Mechanism Augmented INFO Algorithm for Enhanced Controller Design

Abstract

This paper presents a groundbreaking approach to enhance the performance of a vehicle cruise control system—a crucial aspect of road safety. The work offers two key contributions. Firstly, a state-of-the-art metaheuristic algorithm is proposed by augmenting the performance of the weighted mean of vectors (INFO) algorithm using pattern search and elite opposition-based learning mechanisms. The resulting boosted INFO (b-INFO) algorithm surpasses the original INFO, marine predators, and gravitational search algorithms in terms of performance on benchmark functions, including unimodal, multimodal, and fixed-dimensional multimodal functions. Secondly, a novel proportional, fractional order integral, derivative plus double derivative with filter ($$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 ) controller is proposed as a more efficient control structure for vehicle cruise control systems. An objective function is utilized to determine the optimal values for the controller parameters, and the proposed method's performance is compared against a range of recent approaches. Results demonstrate that the b-INFO algorithm-based $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 controller is the most efficient and superior method for controlling a vehicle cruise control system. Moreover, this work represents the first report of a $$P{I}^{\lambda }DN{D}^{2}{N}^{2}$$ P I λ D N D 2 N 2 controller’s implementation for vehicle cruise control systems, underscoring the novelty and significance of this research. The proposed method's exceptional ability is further confirmed by comparisons with the genetic algorithm, ant lion optimizer, atom search optimizer, arithmetic optimization algorithm, slime mold algorithm, Lévy flight distribution algorithm, manta ray foraging optimization, and hunger games search-based proportional–integral–derivative (PID), along with Harris hawks optimization-based PID and fractional order PID controllers. This work marks a remarkable milestone toward safer and more efficient vehicle cruise control systems.

A CNN-based model to count the leaves of rosette plants (LC-Net)

“…In the future, Authors can use many optimization algorithms and embedded them in the network for better accuracy. These algorithms can be any algorithm such as Snake Optimizer (SO) 45 , Fick’s Law Algorithm (FLA) 46 , Jellyfish Search (JS) 47 , Dandelion Optimizer (DO) 48 , Aquila Optimizer 49 – 51 , Atom Search Optimization (ASO) 52 , Water Cycle Algorithm (WCA) 53 , Bald Eagle Search (BES) 54 , African Vultures Optimization Algorithm (AVOA) 55 , Archimedes Optimization Algorithm (AOA) 56 , Beluga Whale Optimization (BWO) 57 , Hunter Prey Optimization (HPO) 58 , INFO 59 , Supply Demand Optimizer 60 , 61 , Reptile Search Algorithm (RSA) 62 , Golden Jackle Optimization (GJO) 63 , and more.…”