A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems

Panda, Arnapurna; Pani, Shantanu Kumar

doi:10.1016/j.asoc.2016.04.030

Cited by 145 publications

(48 citation statements)

References 50 publications

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“…For more details, the reader can refer to [8]. Since the first publication in 2014, the use of SOS in solving different problems has been significantly increased in various research topics [19][20][21][22][23][24][25][26][27]. It shows the potential of SOS algorithm as an attractive tool to search for the optimality.…”

Section: Symbiotic Organisms Searchmentioning

confidence: 99%

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

Prayogo¹,

Cheng²,

Widjaja³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Determining accurate concrete strength is a major civil engineering problem. Test results of 28-day concrete cylinder represent the characteristic strength of the concrete that has been prepared and cast to form the concrete work. It is important to wait 28 days to ensure the quality control of the process, although it is very time consuming. Machine learning techniques are progressively used to simulate the characteristic of concrete materials and have developed into an important research area. This study proposed a comprehensive study using an advanced machine learning technique to predict the compressive strength of concrete from early age test results. In this case, early age test data are being used to get reliable values of the two constants which are required for the prediction. A total of 28 historical cases were used to establish the intelligence prediction model. Obtained results show the performance of the advanced hybrid machine learning technique in predicting the concrete strength with a relatively high accuracy measured by four error indicators. Therefore, the proposed study can offer a high benefit for construction project managers in decision-making processes based on early strength test results.

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Section: Symbiotic Organisms Searchmentioning

confidence: 99%

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

Prayogo¹,

Cheng²,

Widjaja³

et al. 2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

show abstract

“…, otherwise (27) where M is the number of non-dominated solutions obtained, i is an objective function value of a solution in the non-dominated set I, n f…”

Section: Selection Of Best Fit Solutionsmentioning

confidence: 99%

“…Recently, some hybrid multi-objective algorithms have been used by combining the good features of two or more approaches: adaptive hybrid PSO [25], the hybrid multi-objective population migration algorithm [26], Multi-Objective SOS (MOSOS) with an adaptive penalty function [27], non-dominance sort-based Hybrid PSO (HPSO) [28], and Fragmentation-Based Genetic Algorithm (FBGA) [29]. Although there has been considerable research conducted on Pareto-based optimal methods [30][31][32], further study is needed to enhance the convergence and diversity of the approximate Pareto front in the context of cloud computing.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

Anwar

Deng

2018

Applied Sciences

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Cloud computing has emerged as a high-performance computing environment with a large pool of abstracted, virtualized, flexible, and on-demand resources and services. Scheduling of scientific workflows in a distributed environment is a well-known NP-complete problem and therefore intractable with exact solutions. It becomes even more challenging in the cloud computing platform due to its dynamic and heterogeneous nature. The aim of this study is to optimize multi-objective scheduling of scientific workflows in a cloud computing environment based on the proposed metaheuristic-based algorithm, Hybrid Bio-inspired Metaheuristic for Multi-objective Optimization (HBMMO). The strong global exploration ability of the nature-inspired metaheuristic Symbiotic Organisms Search (SOS) is enhanced by involving an efficient list-scheduling heuristic, Predict Earliest Finish Time (PEFT), in the proposed algorithm to obtain better convergence and diversity of the approximate Pareto front in terms of reduced makespan, minimized cost, and efficient load balance of the Virtual Machines (VMs). The experiments using different scientific workflow applications highlight the effectiveness, practicality, and better performance of the proposed algorithm.

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“…Over the past years, SOS has been proven to successfully solve various problems in different fields of research [19][20][21][22]. In surviving environmental change, the living organisms often develop symbiotic interactions among themselves.…”

Section: The Symbiotic Organisms Search (Sos) Symbiotic Organisms Seamentioning

confidence: 99%

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

Prayogo

Cheng

Prayogo

2017

ced

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Abstract:The increasing numbers of design variables and constraints have made many civil engineering problems significantly more complex and difficult for engineers to resolve in a timely manner. Various optimization models have been developed to address this problem. The present paper introduces Symbiotic Organisms Search (SOS), a new nature-inspired algorithm for solving civil engineering problems. SOS simulates mutualism, commensalism, and parasitism, which are the symbiotic interaction mechanisms that organisms often adopt for survival in the ecosystem. The proposed algorithm is compared with other algorithms recently developed with regard to their respective effectiveness in solving benchmark problems and three civil engineering problems. Simulation results demonstrate that the proposed SOS algorithm is significantly more effective and efficient than the other algorithms tested. The proposed model is a promising tool for assisting civil engineers to make decisions to minimize the expenditure of material and financial resources.

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A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems

Cited by 145 publications

References 50 publications

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

Prediction of Concrete Compressive Strength from Early Age Test Result Using an Advanced Metaheuristic-Based Machine Learning Technique

A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

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