An enhanced hybrid arithmetic optimization algorithm for engineering applications

Many new algorithms have been proposed to solve the mathematical equations formulated to describe the real-world problems. But there still does not exist one algorithm that could solve the problems all. And most of the proposed algorithms have defects in some aspects, they need to be improved in application. In order to find a more efficient optimization algorithm and inspired by the better performance of the Arithmetic Optimization algorithm (AOA) and Aquila Optimizer (AO), we proposed a hybridization algorithm of them and abbreviated AOAAO in this paper. Considering the better performance of the Harris Hawk optimization (HHO) algorithm, an energy parameter E was also introduced to balance the exploration and exploitation procedures of individuals in AOAAO swarms, and furthermore, piecewise linear map was introduced to decrease the randomness of the energy parameter. Pseudo code of the proposed AOAAO algorithm was presented, Simulation experiments were carried out on the benchmark functions and three classical engineering problems were also involved in optimization. Nine popular well demonstrated algorithms were included for comparison. Results confirmed the AOAAO would be more efficient in optimization with faster convergence rate, and higher convergence accuracy. 

AOAAO: The Hybrid Algorithm of Arithmetic Optimization Algorithm With Aquila Optimizer

Many new algorithms have been proposed to solve the mathematical equations formulated to describe the real-world problems. But there still does not exist one algorithm that could solve the problems all. And most of the proposed algorithms have defects in some aspects, they need to be improved in application. In order to find a more efficient optimization algorithm and inspired by the better performance of the Arithmetic Optimization algorithm (AOA) and Aquila Optimizer (AO), we proposed a hybridization algorithm of them and abbreviated AOAAO in this paper. Considering the better performance of the Harris Hawk optimization (HHO) algorithm, an energy parameter E was also introduced to balance the exploration and exploitation procedures of individuals in AOAAO swarms, and furthermore, piecewise linear map was introduced to decrease the randomness of the energy parameter. Pseudo code of the proposed AOAAO algorithm was presented, Simulation experiments were carried out on the benchmark functions and three classical engineering problems were also involved in optimization. Nine popular well demonstrated algorithms were included for comparison. Results confirmed the AOAAO would be more efficient in optimization with faster convergence rate, and higher convergence accuracy.

https://ieeexplore.ieee.org/ielx7/6287639/9668973/09684870.pdf

AOAAO: The Hybrid algorithm of Arithmetic Optimization algorithm with Aquila Optimizer

Multilevel Thresholding (MLT) is considered as a significant and imperative research field in image segmentation that can efficiently resolve difficulties aroused while analyzing the segmented regions of multifaceted images with complicated nonlinear conditions. MLT being a simple exponential combinatorial optimization problem is commonly phrased by means of a sophisticated objective function requirement that can only be addressed by nondeterministic approaches. Consequently, researchers are engaging Nature-Inspired Optimization Algorithms (NIOA) as an alternate methodology that can be widely employed for resolving problems related to MLT. This paper delivers an acquainted review related to novel NIOA shaped lately in last three years (2019–2021) highlighting and exploring the major challenges encountered during the development of image multi-thresholding models based on NIOA. 

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Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Arithmetic Optimization Algorithm (AOA) is a physically inspired optimization algorithm that mimics arithmetic operators in mathematical calculation. Although the AOA has an acceptable exploration and exploitation ability, it also has some shortcomings such as low population diversity, premature convergence, and easy stagnation into local optimal solutions. The Golden Sine Algorithm (Gold-SA) has strong local searchability and fewer coefficients. To alleviate the above issues and improve the performance of AOA, in this paper, we present a hybrid AOA with Gold-SA called HAGSA for solving industrial engineering design problems. We divide the whole population into two subgroups and optimize them using AOA and Gold-SA during the searching process. By dividing these two subgroups, we can exchange and share profitable information and utilize their advantages to find a satisfactory global optimal solution. Furthermore, we used the Levy flight and proposed a new strategy called Brownian mutation to enhance the searchability of the hybrid algorithm. To evaluate the efficiency of the proposed work, HAGSA, we selected the CEC 2014 competition test suite as a benchmark function and compared HAGSA against other well-known algorithms. Moreover, five industrial engineering design problems were introduced to verify the ability of algorithms to solve real-world problems. The experimental results demonstrate that the proposed work HAGSA is significantly better than original AOA, Gold-SA, and other compared algorithms in terms of optimization accuracy and convergence speed.

https://www.mdpi.com/2227-7390/10/9/1567/pdf?version=1651828257

A Hybrid Arithmetic Optimization and Golden Sine Algorithm for Solving Industrial Engineering Design Problems

Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

/pdf/an-adaptive-parallel-arithmetic-optimization-algorithm-for-g94v7wssy2.pdf

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm. 

https://unisa.alma.exlibrisgroup.com/view/delivery/61USOUTHAUS_INST/12249849860001831

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

The world's population is gradually aging, and the construction of Service Centers for Seniors (SCS) has become an important issue worthy of concern. In this paper, a particle swarm optimization algorithm with random weight and synchronous learning factor (RSPSO) is proposed to optimize the location and compared with three improved PSO algorithms. Experimental results show that RSPSO bears a faster convergence with better improvements on global searching. Furthermore, it can effectively avoid falling into the local optimal solution. The results also demonstrate the superiority of RSPSO over PSO in location optimization of SCS.

Location Optimization of Service Centers for Seniors Based on an Improved Particle Swarm Optimization Algorithm

/pdf/correction-to-improved-dv-hop-based-on-parallel-and-compact-3jarxmv7.pdf

Correction to: Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

With the increasing complexity and difficulty of practical problems, higher requirements are put forward to optimization techniques, especially the improvement on reliability and performance of meta-heuristic algorithm. In this paper, an improved arithmetic optimization algorithm (IAOA) is proposed, and it is compared with two algorithms—particle swarm optimization (PSO) and arithmetic optimization algorithm (AOA) on 13 benchmark functions. Experimental results show that the proposed algorithm performed better than the compared algorithms in solving particle problems in most cases.

Wei-Feng Wang

Papers

An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

Location Optimization of Service Centers for Seniors Based on an Improved Particle Swarm Optimization Algorithm

Correction to: Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

An Improved Arithmetic Optimization Algorithm with a Strategy Balancing Exploration and Exploitation