Sine-cosine optimization algorithm for the conceptual design of automobile components

Yıldız, Ahmet; Pholdee, Nantiwat; Bureerat, Sujin; Yıldız, Ali Rıza; Sait, Sadiq M.

doi:10.1515/mt-2020-620714

Cited by 25 publications

(12 citation statements)

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“…Yıldız et al [25] used Equilibrium Optimization Algorithm (EOA) to solve a structural design optimization problem for a vehicle seat bracket. Yıldız et al [26] used the Sine Cosine Algorithm (SCA) to solve the shape optimization of a vehicle clutch lever. Panagant et al [27] used Seagull Optimization Algorithm (SOA) to solve the shape optimization of a vehicle bracket.…”

Section: Few Work On Verification Of Performance and Real-world Probl...mentioning

confidence: 99%

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

et al. 2022

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Because of their superior problem-solving ability, nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization problems. Engineering academics have recently focused on meta-heuristic algorithms to solve various optimization challenges. Among the state-of-the-art algorithms, Differential Evolution (DE) is one of the most successful algorithms and is frequently used to solve various industrial problems. Over the previous 2 decades, DE has been heavily modified to improve its capabilities. Several DE variations secured positions in IEEE CEC competitions, establishing their efficacy. However, to our knowledge, there has never been a comparison of performance across various CEC-winning DE versions, which could aid in determining which is the most successful. In this study, the performance of DE and its eight other IEEE CEC competition-winning variants are compared. First, the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions, and the performances have been compared. One unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems, out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite, have been solved to compare the performances. Statistical analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm's ability statistically. Performance analysis exposes that none of the DE variants can solve all the problems efficiently. Performance of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.

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Section: Few Work On Verification Of Performance and Real-world Probl...mentioning

confidence: 99%

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

et al. 2022

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“…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

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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.

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“…New versions of the JAYA algorithm such as chaotic JAYA (Farah & Belazi, 2018), self‐adaptive JAYA (Venkata Rao & Saroj, 2017), modified Jaya algorithm [MJAYA] (Elattar & ElSayed, 2019) and C‐JAYA (Singh & Chaudhary, 2018) tried to overcome its limitations and improve its performance. Sine‐cosine algorithm [SCA] (Mirjalili, 2016a) is certainly a mathematical model based on trigonometric sine and cosine functions, upon which numerous other algorithms have been developed and which have been used on a wide range of engineering domains, including shape optimization in the automobile industry (Yıldız, Pholdee, Bureerat, et al, 2020). Some SCA variants and their applications are reviewed in Abualigah and Diabat (2021), published in 2021.…”

Section: Introductionmentioning

confidence: 99%

Plant competition optimization: A novel metaheuristic algorithm

Rahmani

AliAbdi

2022

Expert Systems

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Plant competition is a fundamental process in plant communities. In one neighbourhood, different plants compete with each other to access shared resources. This paper presents a novel evolutionary algorithm, plant competition optimization (PCO) algorithm, inspired by plant competition processes. In this algorithm, each feasible solution to an optimization problem is assumed to be a plant, with the underlying assumption that each plant grows in competition with its neighbours. In contrast to other techniques inspired by natural phenomena, we attempted to fit the formulation with a known plant growth model, Richards' growth model, and simulate what happens in nature. As the number of plants in a specific area increases, the available resources are decreased, and competition occurs in smaller areas. So, to use this aspect of competition, we develop some mathematical formulation to simulate the decrease of neighbouring area for each plant related to its size to compete on the share resources with the other neighbouring plants. This competition will conduct a smart local search around the most fitted solutions in the optimization context. Furthermore, the reproduction is simulated by producing some seeds in their neighbouring area, which a few of them can migrate to far distances as well. Summing up together, the most powerful plants can grow more. Under competition pressure, their neighbouring area will decrease more than the others, producing more seeds in the next generation. Just as happened in nature, the losers of this competition will die. Our algorithm's efficiency is shown by performing numerical tests on well‐known optimization problems and comparing the results with the other evolutionary algorithms. The results confirm that PCO is effective and efficient in finding sub‐optimal solutions and gives better results than genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), grasshopper optimisation algorithm (GOA), dragonfly algorithm (DA), salp swarm Algorithm (SSA), and comparable results with whale optimization algorithm (WOA) and marine predators algorithm (MPA) on multimodal optimization functions because it efficiently explores the entire search space efficiently and intelligently.

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Sine-cosine optimization algorithm for the conceptual design of automobile components

Cited by 25 publications

References 0 publications

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

Comparative Performance Analysis of Differential Evolution Variants on Engineering Design Problems

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

Plant competition optimization: A novel metaheuristic algorithm

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