Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems

Gupta, Shubham; Abderazek, Hammoudi; Yıldız, Betül Sultan; Yıldız, Ali Rıza; Mirjalili, Seyedali; Sait, Sadiq M.

doi:10.1016/j.eswa.2021.115351

Cited by 111 publications

(23 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, it can be hybridized with other metaheuristic components. Moreover, the proposed HRSA can be investigated to solve other problems such as text clustering, text classification, image enhancement, image segmentation, task scheduling in computing, parameter estimation, forecasting problems, advanced mathematical problems, prediction problems, industrial problems, engineering problems, constrained mechanical design problems, home energy management, wastewater quality parameters, and other real-world problems [38][39][40]. The limitation of this paper is that the used data sets for the data clustering can be real data in the future.…”

Section: Discussionmentioning

confidence: 99%

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Almotairi

Abualigah

2022

Symmetry

View full text Add to dashboard Cite

Data clustering is a complex data mining problem that clusters a massive amount of data objects into a predefined number of clusters; in other words, it finds symmetric and asymmetric objects. Various optimization methods have been used to solve different machine learning problems. They usually suffer from local optimal problems and unbalance between the search mechanisms. This paper proposes a novel hybrid optimization method for solving various optimization problems. The proposed method is called HRSA, which combines the original Reptile Search Algorithm (RSA) and Remora Optimization Algorithm (ROA) and handles these mechanisms’ search processes by a novel transition method. The proposed HRSA method aims to avoid the main weaknesses raised by the original methods and find better solutions. The proposed HRSA is tested on solving various complicated optimization problems—twenty-three benchmark test functions and eight data clustering problems. The obtained results illustrate that the proposed HRSA method performs significantly better than the original and comparative state-of-the-art methods. The proposed method overwhelmed all the comparative methods according to the mathematical problems. It obtained promising results in solving the clustering problems. Thus, HRSA has a remarkable efficacy when employed for various clustering problems.

show abstract

Section: Discussionmentioning

confidence: 99%

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Almotairi

Abualigah

2022

Symmetry

View full text Add to dashboard Cite

show abstract

“…Several studies deal with the evaluation of algorithms on design-engineering problems and the test results are generally encouraging. [26] [27] [28] [29] My goal is to identify additional logistics areas where metaheuristic procedures may have a raison d'être. Furthermore, I am constantly examining the algorithms in order to explore further opportunities and gaps in their application.…”

Section: Discussionmentioning

confidence: 99%

Innovative methods and research directions in the field of logistics

Bíró

Németh

2022

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

By the 21st century, logistics and various supply chains had become key units in the global market and corporate structures. Industry 4.0 has brought developments and implementations to life that have drastically changed and are still changing the practices used in certain areas of logistics. Many new technologies (advanced robotics, additive manufacturing, artificial intelligence (AI), blockchain, drones, Internet of Things (IoT)) have emerged in the digital world, which many companies are using to develop cyber-physical systems in order to increase efficiency, speed, accuracy and the ability to change and steer competition between companies around the world. Planning tasks at the strategic, tactical and operational levels are covered in the areas of production and logistics. The tasks presented here can be identified as extremely complex optimization problems that belong to the np-hard complexity class. These can be addressed in many cases with metaheuristics, and industry also often uses search strategies inspired by biological or physical processes. Metaheuristic algorithms simulate the behavior of a selected phenomenon in a given search area. Algorithms based on various principles can help optimize processes, such as: population-based algorithms, evolutionary methods, behavior-inspired procedures, swarm intelligence methods, etc. New technologies or metaheuristic procedures are also increasingly used in logistics due to the complexity of the tasks. This paper presents theoretical application possibilities of digital transformation, AI and IoT in the field of logistics. The paper provides a further brief overview of the problems surrounding metaheuristics, supported by examples. The article shows the impact of different Industry 4.0 technologies on logistics. There is a shortage of such comprehensive studies, so the article helps provide insight into innovative optimization opportunities in a larger area - the field of logistics. Within this one paper, the impact of new technologies on the field of logistics was collected. A brief description of these will help to identify further directions and deepen the applicability of the new methods in logistics.

show abstract

“…They also mentioned more than 30 novel AI algorithms and their various applications. Particularly, meta-heuristics are suitable for solving real-life engineering design problems, for example, Meng et al, 18 Gupta et al, 19 Yıldız et al, 20 Champasak et al, 21 Yıldız et al, 22 Yıldız et al, 23 Dhiman et al, 24 Yıldız et al, 25 Panagant et al. 26 From the literature, GA and PSO may be the most popular algorithms due to their numerous successes in various problems.…”

Section: Four Algorithmsmentioning

confidence: 99%

Evolutionary artificial intelligence algorithms for the one-way road orientation planning problem with multiple venues: An example of evacuation planning in Taiwan

Hsieh

You

2021

Science Progress

View full text Add to dashboard Cite

Introduction: In large-scale events such as concerts and sports competitions, participants often leave the venue at the same time to return to their respective destinations. Improper traffic planning and traffic light operation usually lead to traffic congestion and road chaos near the sites. Rapid evacuation of participants has become an important issue. Objectives: In this work, a one-way road orientation planning problem with multiple venues is studied in which all roads near the venues are to be scheduled into a one-way orientation with strong connectivity to increase the evacuation efficiency of participants. Methods: In accordance with Robbins’ theorem and a random sequence of integers, an encoding scheme based on module operator is presented to construct a strongly connected graph and plan a one-way orientation for all roads. The proposed encoding scheme is further embedded into four artificial intelligence approaches, namely, grey wolf optimization, immune algorithm, genetic algorithm, and particle swarm optimization, to solve the one-way road orientation planning problem such that the total distance of all vehicles from venues to their destinations is minimized. Results: Numerical results of test problems with multiple venues in Taiwan are provided and analyzed. As shown, all four algorithms can obtain the best solution for the test problems. Conclusions: The new presented encoding scheme with four algorithms can be used to effectively solve the one-way road orientation planning problem for the evacuation of participants. Moreover, grey wolf optimization is superior to the other three algorithms and particle swarm optimization is faster than the other three algorithms.

show abstract

Comparison of metaheuristic optimization algorithms for solving constrained mechanical design optimization problems

Cited by 111 publications

References 49 publications

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Hybrid Reptile Search Algorithm and Remora Optimization Algorithm for Optimization Tasks and Data Clustering

Innovative methods and research directions in the field of logistics

Evolutionary artificial intelligence algorithms for the one-way road orientation planning problem with multiple venues: An example of evacuation planning in Taiwan

Contact Info

Product

Resources

About