A Symbiotic Organisms Search Algorithm for Optimal Allocation of Blood Products

Govender, Prinolan; Ezugwu, Absalom E.

doi:10.1109/access.2018.2886408

Cited by 15 publications

(13 citation statements)

References 28 publications

(62 reference statements)

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“…Two efficient and robust optimization algorithms are adopted to solve the proposed OPF models: teachinglearning-based optimization (TLBO) and symbiotic organisms search (SOS). These algorithms have been recently introduced as powerful stochastic optimization algorithms [14]− [20]. Most nature-inspired metaheuristic algorithms rely on random variables and have some parameters to be fitted to handle the problem under study [16]- [18].…”

Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

“…Both TLBO and SOS have superiority over many different population-based algorithms in terms of rate of convergence, global solution, and computational time [19], [20]. The optimal results obtained by the optimization framework are taken after comparing the output of TLBO and SOS for each loading and weather condition.…”

Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

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Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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The complexity of achieving optimal power flow in the presence of renewable resources decreases the accuracy and optimality levels of the power system due to the associated intermittency and uncertainty. The increased challenge of large-scale deployment of wind energy necessitates the proper modeling of wind impact on power system security and reliability levels. This article discusses a new reliable power flow optimization tool that accounts for wind power availability and uncertainty. An accurate wind forecast model is created to maintain power system security considering wind power variability. The error of the forecasting phase is included in the proposed model to accurately predict the available wind power. In this work, the scattered wind data is converted into informative frequency distribution considering the effect of averaging around integers, halves, and quarters. The proposed method maximizes the utilization level of wind energy without deteriorating the system security. The accuracy of the new proposed work is presented by comparing its results with other models discussed in the literature. A complete and integrated formulation of the objective function has been accomplished. The cost function includes transmission losses, generation operating costs, generation gas emissions, and valve-point effects. Reliable and efficient optimization algorithms are adopted to minimize the established cost function of the system ⎯namely, teaching-learningbased optimization and symbiotic organisms search algorithms. The effectiveness of the proposed approach is validated using the IEEE 39-bus system. INDEX TERMS Generation gas emission, generation operating cost, optimal power flow, symbiotic organisms search algorithm, valve-point effects, wind speed forecast.

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Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

Section: The Employment Of the Candidate Optimization Algorithms mentioning

confidence: 99%

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

et al. 2020

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“…It must be remembered that blood is donated voluntarily to blood banks; therefore, there are no constant levels of supply, and while there is a daily demand for blood, it may vary. The BAP is thus said to be NP-hard, simply the supply and demand of blood involve some complexities, which are associated with blood compatibility issues and the stochastic nature of the daily needs for such a product [10]. The BAP is, therefore, studied to develop an adequate model that utilizes whole blood units more efficiently and achieves the desired objective function of minimizing unnecessary blood product importation from an external source.…”

Section: Introductionmentioning

confidence: 99%

“…An enormous amount of research has involved models relating to the policies of reducing blood wastages [2], [10]- [14]. These efforts have considered instances of double-cross matching of blood and alternative compatibility decision making in using Rh-negative blood for compatible Rh-positive patients in the event of scarcity.…”

Section: Introductionmentioning

confidence: 99%

“…In many instances, this new wave of metaheuristic approaches has produced the best solutions for some of the unsolved benchmark problem sets [44]. For example, the genetic algorithm (GA), particle swarm optimization (PSO) algorithm and the more recent symbiotic organisms search algorithm (SOS) are among some of the metaheuristic techniques that have shown success in solving the blood assignment problem [10], [25], [26], [31], [49], [50]. Therefore, the current study considers the possibility of combining these three algorithms to solve a similar problem with the intent to obtain even more superior results.…”

Section: Introductionmentioning

confidence: 99%

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Computational Intelligence Approach to Dynamic Blood Allocation With ABO-Rhesus Factor Compatibility Under Real-World Scenario

et al. 2020

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The supply chain management of blood products specifically deals with the aspect of efficient planning, implementing, and controlling of the in and out flow processes of blood unity in the blood bank system. Therefore, any improvement in the management of these chains would directly influence the manner in which blood and blood products are distributed to all the various sectors requiring such scarce and precious resources. Generally, the management of blood products is difficult due to the four ABO blood groups, which is further complicated by positive/negative rhesus factors. In this paper, a more simplified and robust dynamic mathematical model is presented for the efficient management of the blood bank. The corresponding sets of governing equations from an existing model are extended to cover the rhesus factors and the solution methods of the newly derived equations are subsequently investigated. In addition, a mathematical representation of the decision making process is presented as a function of the blood bank stockpile. Furthermore, in order to demonstrate the robustness of the developed model and to provide managerial insights, a new global hybrid symbiotic organisms search genetic particle swarm optimization algorithm is developed. Several numerical computations are performed using real-world datasets from the Enugu National Blood Transfusion Centre, in Nigeria, which fall within the monthly initial blood volume bounds of 300 over a period of eight years (2010-2018). Finally, the experimental results show that the mathematical model and metaheuristic optimization method proposed in this paper offer a better solution approach for blood allocation in dynamic environments. More so, the impact of some essential control parameters on the results are analysed to help the blood bank managers or decision makers to select accurately the desired parameters for optimal results yield.

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