Multiobjective adaptive symbiotic organisms search for truss optimization problems

Tejani, Ghanshyam G.; Pholdee, Nantiwat; Bureerat, Sujin; Prayogo, Doddy

doi:10.1016/j.knosys.2018.08.005

Cited by 91 publications

(30 citation statements)

References 14 publications

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“…Many studies to develop algorithms with adaptation for multiobjective optimization are also found in the literature. Similarly, Tejani et al [52] utilized multiobjective adaptive symbiotic organisms search for truss optimization problems. Bingul [2] proposed an adaptive GA with dynamic fitness function for multiobjective design problems.…”

Section: Modification In Mutualism Phasementioning

confidence: 99%

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Modified symbiotic organisms search for structural optimization

Kumar

Tejani²,

Mirjalili

2018

Engineering with Computers

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The structural dynamic response predominantly depends upon natural frequencies which fabricate these as a controlling parameter for dynamic response of the truss. However, truss optimization problems subjected to multiple fundamental frequency constraints with shape and size variables are more arduous due to its characteristics like non-convexity, nonlinearity, and implicit with respect to design variables. In addition, mass minimization with frequency constraints are conflicting in nature which intricate optimization problem. Using meta-heuristic for such kind of problem requires harmony between exploration and exploitation to regulate the performance of the algorithm. This paper proposes a modification of a nature inspired Symbiotic Organisms Search (SOS) algorithm called a Modified SOS (MSOS) algorithm to enhance its efficacy of accuracy in search (exploitation) together with exploration by introducing an adaptive benefit factor and modified parasitism vector. These modifications improved search efficiency of the algorithm with a good balance between exploration and exploitation, which has been partially investigated so far. The feasibility and effectiveness of proposed algorithm is studied with six truss design problems. The results of benchmark planar/space trusses are compared with other meta-heuristics. Complementarily the feasibility and effectiveness of the proposed algorithms are investigated by three unimodal functions, thirteen multimodal functions, and six hybrid functions of the CEC2014 test suit. The experimental results show that MSOS is more reliable and efficient as compared to the basis SOS algorithm and other state-of-the-art algorithms. Moreover, the MSOS algorithm provides competitive results compared to the existing meta-heuristics in the literature.

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Section: Modification In Mutualism Phasementioning

confidence: 99%

“…Tran et al [51] introduced opposition multiple objective SOS for scheduling repetitive projects. Similarly, Tejani et al [52] utilized multiobjective adaptive symbiotic organisms search for truss optimization problems. Despite of various advantages, above-mentioned algorithms lacks in key components.…”

Section: Introductionmentioning

confidence: 99%

Modified symbiotic organisms search for structural optimization

Kumar

Tejani²,

Mirjalili

2018

Engineering with Computers

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“…In [10,11], SOS has been applied to solve the optimal power flow and the economic emission load dispatch problems. Moreover, SOS has been successfully applied in the optimal design of linear and circular antenna arrays [12,13], in solving the load frequency control problem [14], in structure optimization problems [15], in solving unconstrained function optimization [16], in the design of an improved 3D Turbo Code [17], in the optimal design of analog active filters [18], in the synthesis of elliptical antenna arrays [19], and other engineering areas [20][21][22][23][24]. The results indicated that SOS gives very good results and is very competitive with the state of the art for the solution of these problems, which motivated this work.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

Dib¹,

Al-Sammarraie²

2020

IJECE

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This paper investigates the optimal design of symmetric switching CMOS inverter using the Symbiotic Organisms Search (SOS) algorithm. SOS has been recently proposed as an effective evolutionary global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the three common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other optimization methods, SOS has no parameters to be tuned, which makes it an attractive and easy-to-implement optimization method. Here, SOS is used to design a high speed symmetric switching CMOS inverter, which is considered the most fundamental logic gate. SOS results are compared to those obtained using several optimization methods, like particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), and other ones, available in the literature. It is shown that the SOS is a robust straight-forward evolutionary algorithm that can compete with other well-known advanced methods.

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“…Metaheuristic algorithm-based methods are also attractive when solving structural topology optimization problems [5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Firefly Algorithm Based Hybrid Method for Structural Topology Optimization

Gebremedhen

Woldemichael

Hashim

2020

Preprint

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In this paper, a firefly algorithm based hybrid algorithm through retaining global convergence of firefly algorithm and ability to generate connected topologies of optimality criteria (OC) method is proposed as an alternative method to solve stress-based topology optimization problems. The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. Input parameters, the number of fireflies, and the number of function evaluations were determined before the implementation of the firefly algorithm to solve formulated problems. Since the direct application of the firefly algorithm cannot generate connected topologies, outputs from the firefly algorithm were used as an initial input material distribution for the OC method. The proposed method was validated using two-dimensional benchmark problems and the results were compared with results using the OC method. Weight percentage reduction, maximum stress-induced, optimal material distribution, and compliance were used to compare results. Results from the proposed method showed that the proposed method can generate connected topologies which are free from the interference of end-users, and only depend on boundary conditions or design variables. From the results, the objective function (weight of the design domain) can be further reduced in the range of 5% to 15% compared to the OC method.

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Multiobjective adaptive symbiotic organisms search for truss optimization problems

Cited by 91 publications

References 14 publications

Modified symbiotic organisms search for structural optimization

Modified symbiotic organisms search for structural optimization

Optimal design of symmetric switching CMOS inverter using symbiotic organisms search algorithm

A Firefly Algorithm Based Hybrid Method for Structural Topology Optimization

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