Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times

Ezugwu, Absalom E.; Adeleke, O. J.; Viriri, Serestina

doi:10.1371/journal.pone.0200030

Cited by 45 publications

(25 citation statements)

References 37 publications

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“…We compare the performance of MHHO with a set of method, including invasive weeds optimization (IWO) [32], Artificial Bee Colony (ABC) [77], metaheuristic for randomized priority search (Meta-RaPS) [32], improved firefly (IFA) [32], Hybrid ABC (HABC) [77], Tabu Search (TS) [32], RSA [78], partition heuristic (PH) [32], Genetic Algorithm (GA) [32], Ant Colony Optimization (ACO) [32], extended ACO (ACOII) [32], GADP [55], basic Simulated Annealing (SA) [78], FA [32], SOS with the longest processing time first (LPT) rule (SOS-LPT) [79], symbiotic organisms search (SOS) [79], HSOSSA [30], ESA [30], and ESOS [30].…”

Section: Comparison With Other Mh Methods Using Large-size Problemsmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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Scheduling can be described as a decision-making process. It is applied in various applications, such as manufacturing, airports, and information processing systems. More so, the presence of symmetry is common in certain types of scheduling problems. There are three types of parallel machine scheduling problems (PMSP): uniform, identical, and unrelated parallel machine scheduling problems (UPMSPs). Recently, UPMSPs with setup time had attracted more attention due to its applications in different industries and services. In this study, we present an efficient method to address the UPMSPs while using a modified harris hawks optimizer (HHO). The new method, called MHHO, uses the salp swarm algorithm (SSA) as a local search for HHO in order to enhance its performance and to decrease its computation time. To test the performance of MHHO, several experiments are implemented using small and large problem instances. Moreover, the proposed method is compared to several state-of-art approaches used for UPMSPs. The MHHO shows better performance in both small and large problem cases.

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Section: Comparison With Other Mh Methods Using Large-size Problemsmentioning

confidence: 99%

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

et al. 2020

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“…This method was shown to possess the sufficient descent property and globally convergent with σ < 1 3 . Zhang [34] modified (11) and (12) to give new variants, which are referred to as MVPRP and MVHS here, respectively, with the update parameters given as…”

Section: Related Studiesmentioning

confidence: 99%

“…For instance, their applications take central place in the well studied vehicle routing problem, a problem frequently encountered by the distribution units of manufacturing industries (see [2]). Their importance has necessitated the design of many techniques that can be deployed for solving both constrained and unconstrained optimization problems (see [11,12]). In this paper, however, we consider an optimization technique known as the conjugate gradient (CG) method for solving unconstrained optimization problems of the form…”

Section: Introductionmentioning

confidence: 99%

A New Family of Hybrid Conjugate Gradient Methods for Unconstrained Optimization

Adeleke

Ezugwu

Osinuga

2020

Stat., optim. inf. comput.

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The conjugate gradient method is a very efficient iterative technique for solving large-scale unconstrainedoptimization problems. Motivated by recent modifications of some variants of the method and construction of hybrid methods, this study proposed four hybrid methods that are globally convergent as well as computationally efficient. The approach adopted for constructing the hybrid methods entails projecting ten recently modified conjugate gradient methods. Each of the hybrid methods is shown to satisfy the descent property independent of any line search technique and globally convergent under the influence of strong Wolfe line search. Results obtained from numerical implementation of these methods and performance profiling show that the methods are very competitive with well-known traditional methods.

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“…SOS is capable of providing an efficient and robust approach in exploiting and exploring large search space. More so, it has been employed to optimize a number of combinatorial optimization problems and has proved to be an efficient performer in that aspect [30], [41], [42], [43], [44]. Therefore, the potential of SOS in finding good quality solutions to most real-world optimization problems makes it attractive for further investigation.…”

Section: Introductionmentioning

confidence: 99%

A Symbiotic Organisms Search Algorithm for Optimal Allocation of Blood Products

Govender

Ezugwu

2019

IEEE Access

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The blood assignment problem is an important real-world optimization problem because of the continuous demand for blood transfusion during medical emergencies. The formulation of this problem stretches from managing critical blood shortage levels and blood unit expiration, to blood compatibility between donor and patients. Another contributing factor to the blood assignment problem lies in the blood bank having to import additional blood units from external sources when supply cannot meet the demand. These challenges have serious consequences especially in the case where the demand for the blood is very high. Therefore, there is the need to minimize blood product wastage with regards to expiration and importation, whilst maximizing product delivery to patients in need. To solve this problem, existing studies used fixed percentage bounds to generate values for demand and supply of whole blood units. However, in this paper, a different approach is considered when generating such values, which involves allocating a unique percentage bound to each month. The bounds conform to statistics taken from South African social behavior with the attempt at generating values which mimic real-life monthly demand and supply of whole blood units. Furthermore, this study implements a hybrid metaheuristic algorithm that combines symbiotic organisms search algorithm with the blood assignment policy in relation to the blood banks of South Africa to effectively minimize the operational costs of the blood transfusion centers. The computational results indicate that the proposed algorithm performed quite satisfactorily from the computational time, stockpiling , and low importation level points of view. INDEX TERMS Blood assignment problem, blood product, blood group, blood compatibility, symbiotic organisms search algorithm, SOS.

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Symbiotic organisms search algorithm for the unrelated parallel machines scheduling with sequence-dependent setup times

Cited by 45 publications

References 37 publications

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

Modified Harris Hawks Optimizer for Solving Machine Scheduling Problems

A New Family of Hybrid Conjugate Gradient Methods for Unconstrained Optimization

A Symbiotic Organisms Search Algorithm for Optimal Allocation of Blood Products

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