Running city game optimizer: a game-based metaheuristic optimization algorithm for global optimization

Ma Bing,Hu Yongtao,Lu Pengmin,Liu Yonggang 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1

As science and technology improve, more and more complex global optimization difficulties arise in real-life situations. Finding the most perfect approximation and optimal solution using conventional numerical methods is intractable. Metaheuristic optimization approaches may be effective in achieving powerful global optimal solutions for these complex global optimization situations. Therefore, this paper proposes a new game-based algorithm called the running city game optimizer (RCGO), which mimics the game participant’s activity of playing the running city game. The RCGO is mathematically established by three newfangled search strategies: siege, defensive, and eliminated selection. The performance of the proposed RCGO algorithm in optimization is comprehensively evaluated on a set of 76 benchmark problems and 8 engineering optimization scenarios. Statistical and comparative results show that RCGO is more competitive with other state-of-the-art competing approaches in terms of solution quality and convergence efficiency, which stems from a proper balance between exploration and exploitation. Additionally, in the case of engineering optimization scenarios, the proposed RCGO is able to deliver superior fitting and occasionally competitive outcomes in optimization applications. Thus, the proposed RCGO is a viable optimization tool to easily and efficiently handle various optimization problems.

A novel hippo swarm optimization: for solving high-dimensional problems and engineering design problems

Zhou Guoyuan,Du Jiaxuan,Guo Jia,Li Guoliang 한국CDE학회 2024 Journal of computational design and engineering Vol.11 No.3

In recent years, scholars have developed and enhanced optimization algorithms to tackle high-dimensional optimization and engineering challenges. The primary challenge of high-dimensional optimization lies in striking a balance between exploring a wide search space and focusing on specific regions. Meanwhile, engineering design problems are intricate and come with various constraints. This research introduces a novel approach called Hippo Swarm Optimization (HSO), inspired by the behavior of hippos, designed to address high-dimensional optimization problems and real-world engineering challenges. HSO encompasses four distinct search strategies based on the behavior of hippos in different scenarios: starvation search, alpha search, margination, and competition. To assess the effectiveness of HSO, we conducted experiments using the CEC2017 test set, featuring the highest dimensional problems, CEC2022 and four constrained engineering problems. In parallel, we employed 14 established optimization algorithms as a control group. The experimental outcomes reveal that HSO outperforms the 14 well-known optimization algorithms, achieving first average ranking out of them in CEC2017 and CEC2022. Across the four classical engineering design problems, HSO consistently delivers the best results. These results substantiate HSO as a highly effective optimization algorithm for both high-dimensional optimization and engineering challenges.

Challenges and opportunities in green hydrogen supply chain through metaheuristic optimization

Gorji Saman A 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.3

A comprehensive analysis of the green hydrogen supply chain is presented in this paper, encompassing production, storage, transportation, and consumption, with a focus on the application of metaheuristic optimization. The challenges associated with each stage are highlighted, and the potential of metaheuristic optimization methods to address these challenges is discussed. The primary method of green hydrogen production, water electrolysis through renewable energy, is outlined along with the importance of its optimization. Various storage methods, such as compressed gas, liquid hydrogen, and material-based storage, are covered with an emphasis on the need for optimization to improve safety, capacity, and performance. Different transportation options, including pipelines, trucks, and ships, are explored, and factors influencing the choice of transportation methods in different regions are identified. Various hydrogen consumption methods and their associated challenges, such as fuel cell performance optimization, hydrogen-based heating systems design, and energy conversion technology choice, are also discussed. The paper further investigates multi-objective approaches for the optimization of problems in this domain. The significant potential of metaheuristic optimization techniques is highlighted as a key to addressing these challenges and improving overall efficiency and sustainability with respect to future trends in this rapidly advancing area.

A Modified Big Bang-Big Crunch Algorithm for Structural Topology Optimization

Hong-Kyun Ahn,한동석,한석영 한국정밀공학회 2019 International Journal of Precision Engineering and Vol.20 No.12

The purpose of this study is to develop a topology optimization scheme based on big bang–big crunch (BB–BC) algorithm, inspired from the evolution of the universe called big bang and big crunch theory. In order to apply the BB–BC algorithm to topology optimization for static and dynamic stiffness problems, the parameters of the algorithm were transformed to those of topology optimization scheme. In addition, some parameters such as big bang (BB) range, BB search, population and non-exchange limit were newly introduced to topology optimization scheme. Also, a parametric study for the parameters involved in the topology optimization scheme was performed to reduce the number of parameters, and find the appropriate ranges for topology optimization. Some examples were provided to examine the effectiveness of the developed topology optimization scheme for both static and dynamic stiffness problems throughout comparing with other metaheuristic topology optimization algorithms and the BESO (bi-directional evolutionary structural optimization) method. It was verified that the suggested algorithm shows superior to the compared typical metaheuristic topology optimization algorithms in the viewpoints of stability, robustness, accuracy and the convergence rate.

A cooperative particle swarm optimizer with stochastic movements for computationally expensive numerical optimization problems

Ngo, T.T.,Sadollah, A.,Kim, J.H. Elsevier 2016 Journal of computational science Vol.13 No.-

<P>Nature is the rich principal source for developing optimization algorithms. Metaheuristic algorithms can be classified with the emphasis on the source of inspiration into several categories such as biology, physics, and chemistry. The particle swarm optimization (PSO) is one of the mostwell-known bio-inspired optimization algorithms which mimics movement behavior of animal flocks especially bird and fish flocking. In standard PSO, velocity of each particle is influenced by the best individual and its best personal experience. This approach could make particles trap into the local optimums and miss opportunities of jumping to far better optimums than the currents and sometimes causes fast premature convergence. To overcome this issue, a new movement concept, so called extraordinariness particle swarm optimizer (EPSO) is proposed in this paper. The main contribution of this study is proposing extraordinary motion for particles in the PSO. Indeed, unlike predefined movement used in the PSO, particles in the EPSO can move toward a target which can be global best, local bests, or even the worst individual. The proposed improved PSO outperforms than the standard PSO and its variants for benchmarks such as CEC 2015 benchmarks. In addition, several constrained and engineering design problems have been tackled using the improved PSO and the optimization results have been compared with the standard PSO, variants of PSO, and other optimizers. (C) 2016 Elsevier B.V. All rights reserved.</P>

A modified smell agent optimization for global optimization and industrial engineering design problems

Wang Shuang,Hussien Abdelazim G,Kumar Sumit,AlShourbaji Ibrahim,Hashim Fatma A 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6

This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active–passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration–exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10–20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously.

Optimization of Large-Scale Frame Structures Using Fuzzy Adaptive Quantum Inspired Charged System Search

Siamak Talatahari,Mahdi Azizi,Mehdi Toloo,Milad Baghalzadeh Shishehgarkhaneh 한국강구조학회 2022 International Journal of Steel Structures Vol.22 No.3

In this paper, a metaheuristic-based design approach is developed in which the structural design optimization of large-scale steel frame structures is concerned. Although academics have introduced form-dominant methods, yet using artifi cial intelligence in structural design is one of the most critical challenges in recent years. However, the Charged System Search (CSS) is utilized as the primary optimization approach, which is improved by using the main principles of quantum mechanics and fuzzy logic systems. In the proposed Fuzzy Adaptive Quantum Inspired CSS algorithm, the position updating procedure of the standard algorithm is developed by implementing the center of potential energy presented in quantum mechanics into the general formulation of CSS to enhance the convergence capability of the algorithm. Simultaneously, a fuzzy logic-based parameter tuning process is also conducted to enhance the exploitation and exploration rates of the standard optimization algorithm. Two 10 and 60 story steel frame structures with 1026 and 8272 structural members, respectively, are utilized as design examples to determine the performance of the developed algorithm in dealing with complex optimization problems. The overall capability of the presented approach is compared with the Charged System Search and other metaheuristic optimization algorithms. The proposed enhanced algorithm can prepare better results than the other metaheuristics by considering the achieved results.

시뮬레이티드 어닐링와 타부 검색 알고리즘을 활용한 포트폴리오 연구

이우식 한국산업융합학회 2024 한국산업융합학회 논문집 Vol.27 No.2

Metaheuristics' impact is profound across many fields, yet domestic financial portfolio optimization research falls short, particularly in asset allocation. This study delves into metaheuristics for portfolio optimization, examining theoretical and practical benefits. Findings indicate portfolios optimized via metaheuristics outperform the Dow Jones Index in Sharpe ratios, underscoring their potential to enhance risk-adjusted returns significantly. Tabu search, in comparison to Simulated Annealing, demonstrates superior performance by efficiently navigating the search space. Despite these advancements, practical application remains challenging due to the complexities in metaheuristic implementation. The study advocates for broader algorithmic exploration, including population-based metaheuristics, to refine asset allocation strategies further. This research marks a step towards optimizing portfolios from an extensive array of financial assets, aiming for maximum efficacy in investment outcomes.

A novel chaotic Runge Kutta optimization algorithm for solving constrained engineering problems

Yıldız Betül Sultan,Mehta Pranav,Panagant Natee,Mirjalili Seyedali,Yildiz Ali Riza 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.6

This study proposes a novel hybrid metaheuristic optimization algorithm named chaotic Runge Kutta optimization (CRUN). In this study, 10 diverse chaotic maps are being incorporated with the base Runge Kutta optimization (RUN) algorithm to improve their performance. An imperative analysis was conducted to check CRUN’s convergence proficiency, sustainability of critical constraints, and effectiveness. The proposed algorithm was tested on six well-known design engineering tasks, namely: gear train design, coupling with a bolted rim, pressure vessel design, Belleville spring, and vehicle brake-pedal optimization. The results demonstrate that CRUN is superior compared to state-of-the-art algorithms in the literature. So, in each case study, CRUN was superior to the rest of the algorithms and furnished the best-optimized parameters with the least deviation. In this study, 10 chaotic maps were enhanced with the base RUN algorithm. However, these chaotic maps improve the solution quality, prevent premature convergence, and yield the global optimized output. Accordingly, the proposed CRUN algorithm can also find superior aspects in various spectrums of managerial implications such as supply chain management, business models, fuzzy circuits, and management models.

Simulation-based optimization for design parameter exploration in hybrid system: a defense system example

Hong, Jeong Hee,Seo, Kyung-Min,Kim, Tag Gon SAGE Publications 2013 Simulation Vol.89 No.3

<P>This paper presents a method for solving the optimization problems that arise in hybrid systems. These systems are characterized by a combination of continuous and discrete event systems. The proposed method aims to find optimal design configurations that satisfy a goal performance. For exploring design parameter space, the proposed method integrates a metamodel and a metaheuristic method. The role of the metamodel is to give good initial candidates and reduced search space to the metaheuristic optimizer. On the other hand, the metaheuristic method improves the quality of the given candidates. This proposal also demonstrates a defense system that illustrates the practical application of the presented method. The optimization objective of the case study is to find the required operational capability configurations of a decoy that meet the desired measure of effectiveness. Through a comparison with a full search method, two metamodeling methods without the aid of metaheuristics and a metaheuristic method without the support of metamodels, we confirmed that the proposed method provides same high-quality solutions as those of the full search method at a small computational cost.</P>