• Journal of Computational Design and Engineering
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• v.3 no.3
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• pp.226-249
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• 2016

The symbiotic organisms search (SOS) algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations. Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis. The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3516-3541
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• 2018

Task scheduling is one of the most challenging aspects of cloud computing nowadays, and it plays an important role in improving overall performance in, and services from, the cloud, such as response time, cost, makespan, and throughput. A recent cloud task-scheduling algorithm based on the symbiotic organisms search (SOS) algorithm not only has fewer specific parameters, but also incurs time complexity. SOS is a newly developed metaheuristic optimization technique for solving numerical optimization problems. In this paper, the basic SOS algorithm is reduced, and chaotic local search (CLS) is integrated into the reduced SOS to improve the convergence rate. Simulated annealing (SA) is also added to help the SOS algorithm avoid being trapped in a local minimum. The performance of the proposed SA-CLS-SOS algorithm is evaluated by extensive simulation using the Matlab framework, and is compared with SOS, SA-SOS, and CLS-SOS algorithms. Simulation results show that the improved hybrid SOS performs better than SOS, SA-SOS, and CLS-SOS in terms of convergence speed and makespan.

• Advances in Energy Research
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• v.4 no.4
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• pp.255-274
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• 2016

This paper presents a novel symbiotic organisms search (SOS) algorithm to optimize both real power loss (RPL) and voltage stability limit (VSL) of a transmission network by controlling the variables such as unified power flow controller (UPFC) location, UPFC series injected voltage magnitude and phase angle and transformer taps simultaneously. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained multi objective, multi variable optimization problem with a fitness function integrating both RPL and VSL. The symbiotic organisms search (SOS) algorithm is a nature inspired optimization method based on the biological interactions between the organisms in ecosystem. The advantage of SOS algorithm is that it requires a few control parameters compared to other meta-heuristic algorithms. The proposed SOS algorithm is applied for solving optimum control variables for both single objective and multi-objective optimization problems and tested on New England 39 bus test system. In the single objective optimization problem only RPL minimization is considered. The simulation results of the proposed algorithm have been compared with the results of the algorithms like interior point successive linear programming (IPSLP) and bacteria foraging algorithm (BFA) reported in the literature. The comparison results confirm the efficacy and superiority of the proposed method in optimizing both single and multi objective problems.

• Bulletin of the Korean Mathematical Society
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• v.49 no.3
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• pp.517-528
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• 2012

This paper is estimating the domain of attraction for a class of susceptible-exposed-infectious-recovered (SEIR) epidemic dynamic models by using sum of squares optimization. First, the stability is analyzed for the equilibriums of SEIR model, and the domain of attraction in the endemic equilibrium is estimated by using sum of squares optimization. Finally, a numerical example is examined.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.10
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• pp.1430-1437
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• 2011

Synthesis conditions of cocoa butter equivalents were optimized using the response surface method (RSM) by interesterification of canola oil (Ca), palmitic ethyl ester (PEE), and stearic ethyl ester (StEE). The reaction was catalyzed by immobilized lipase (Lipozyme TLIM) from Thermomyces lanuginosa to produce structured lipids containing a composition of triacylglycerols similar to cocoa butter. Reaction conditions were optimized using D-optimal design with the three reaction factors of the substrate molar ratio of canola oil to palmitic ethyl ester and stearic ethyl ester (Ca : PEE : StEE=1:1:3, 1:1.66:5, 1:2:6, 1:2.33:7, 1:3:9, $X_1$), enzyme ratio (2~6%, $X_2$), and reaction time (30~270 min, $X_3$). The optimal conditions that minimized acyl-migration while maximizing 1-palmitoyl-2-oleoyl-3-stearoyl glycerol (POS), 1,3-distearoyl-2-oleoyl glycerol (SOS), and 1,3-dipalmitoyl-2-oleoyl glycerol (POP) were predicted, resulting in Ca : PEE : StEE=1:3:9, 6% of enzyme ratio, and 40 min of reaction time. The reaction product of structured lipids was synthesized again under the same conditions, showing 10.43 area% of acyl-migration, 25.31 area% of POS/PSO, 19.79 area% of SOS, and 11.22 area% of POP.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.7
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• pp.975-980
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• 2011

1-Palmitoyl-2-oleoyl-3-oleoyl glycerol (POO) and 1-palmitoyl-2-oleoyl-3-palmitoyl glycerol (POP) were enriched from palm stearin using an acetone fractionation process. Response surface methodology was employed to optimize the purity of POO ($Y_1$, %) and POP ($Y_2$, %) along with POO+POP content ($Y_3$, g) based on independent variables such as fractionation temperature ($X_1$, 25, 30, and $35^{\circ}C$) and the ratio of palm stearin to acetone ($X_2$, 1:3, 1:6 and 1:9, w/v). Fractionation conditions were optimized to maximize $Y_1$, $Y_2$, and $Y_3$, in which fractionation temperature was $29.3^{\circ}C$ with a 1:5.7 acetone ratio. With such parameters, 60.9% of POP and 23.8% of POO purity were expected with a 75% yield (3.0 g) of POO and POP.