A novel optimization booster algorithm

Pakzad-Moghaddam, S.H.; Mina, Hassan; Mostafazadeh, Parisa

doi:10.1016/j.cie.2019.07.046

Cited by 18 publications

(3 citation statements)

References 62 publications

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“…Thus, there would be no possibility to solve large-scale problems using GAMS software. Accordingly, the researchers interested in this field are recommended to solve the proposed problem on a large scale using heuristic, meta-heuristic, or optimization booster algorithms (Pakzad-Moghaddam et al, 2019). Moreover, since a supplier as an upstream in SCN plays a significant role in chain performance, it is suggested that the supplier selection problem be added to the SCN under study using a triple bottom line (economic, environmental, and social attributes) to result in the proposal of a sustainable, perishable pharmaceutical SCN.…”

Section: Discussionmentioning

confidence: 99%

A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design

et al. 2020

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In this paper, a bi-objective mixed-integer linear programming (MILP) model is formulated for designing a perishable pharmaceutical supply chain network (SCN) under demand uncertainty. The objectives of the proposed model are to simultaneously minimize the total cost of the network and lost demand amount. The proposed model is multi-product and multi-period and includes simultaneous facilities location, vehicle routing, and inventory management; hence, it is considered an operational-strategic model. Procurement discounts, the lifetime of products, storing products for future periods, lost demand, and soft and hard time windows are the main assumptions of the proposed model. A novel hybrid approach, based on fuzzy theory, chance constrained programming, and goal programming approach, is developed for solving the proposed bi-objective model. The validity of the proposed model and developed solution approach is evaluated using data from Avonex, a prefilled syringe distribution chain serving 11 health centers in Tehran. The proposed model indicates that some lost sales exist, and to overcome the lost sales, the case company needs to invest a little more in addition to the initial investment of around 2 billion tomans. The results obtained from implementing the model and the sensitivity analysis, using realworld data, confirm the efficiency and validity of the proposed mathematical model and solution approach.

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Section: Discussionmentioning

confidence: 99%

A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design

et al. 2020

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“…Many algorithms have been presented in recent years, including particle-swarm optimization (PSO) , Harris Hawk's optimization (HHO) (Heidari et al, 2019) ant-colony optimization (ACO) (Karaboga & Basturk, 2007), pathfinder algorithm (PFA) (Yapici & Cetinkaya, 2019), poor and rich optimization algorithm (PRO) (Samareh Moosavi & Bardsiri, 2019), Gaussian quantum-behaved particle-swarm optimization (QPSO) (Coelho, 2010), weighted differential-evolution algorithm (WDE) (Civicioglu et al, 2020), particleswarm optimization+(PSO+) (Kohler et al, 2019) and optimization booster algorithm (OBA) (Pakzad-Moghaddam et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Truss Using a Hybrid Method Based on Particle Swarm Optimizer and Harris Hawk Algorithm

Yassami

2024

JJCE

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This paper presents two hybrid optimization methods known as PSOHHO and DPSOHHO optimization algorithms. In the first method, using a number of formulae, the top populations are exchanged between the two algorithms and a new population is created and in the second method, we adopted the parallel optimization and optimized its performance. In this method, unlike other parallel methods, the population does not remain constant. With this ability, the strengths of an algorithm can be used to compensate for the weaknesses of the other algorithm. In these methods, no changes are made to the algorithms. The main goal is to use existing algorithms. These methods attain the optimal solution in the shortest time possible. Two algorithms of particleswarm optimization (PSO) and Harris Hawks's optimization (HHO) are used to present this method and two truss samples and CEC209 are considered to confirm the performance of this method. Based on the results, these methods have rapid convergence speed and acceptable results compared to other methods. KEYWORDS: Meta-heuristic algorithms, Hybrid algorithm, Optimization, Dynamic hybrid algorithm, Truss.

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“…In recent decades, many researchers have used naturebased metaheuristic (MH) optimization algorithms to solve engineering problems. Some of these algorithms include tabu search (Glover, 1997), simulated annealing (Kirkpatrick et al, 1983), genetic algorithm (Goldberg and Holland, 1988), particle swamp optimization (Eberhart and Kennedy, 1995), ant colony algorithm (Dorigo et al, 1996), harmony search (HS) (Geem et al, 2001), big bang-big crunch (Erol and Eksin, 2006), the artificial bee colony algorithm (Karaboga and Basturk, 2007), cuckoo search (Yang and Deb, 2009), firefly algorithm (FA) (Yang, 2009), cuckoo optimization algorithm (Rajabioun, 2011), teaching-learning-based optimization (Rao et al, 2011), flower pollination algorithm (Yang, 2012), water cycle algorithm (Eskandar et al, 2012), krill herd algorithm (Gandomi and Alavi, 2012), ray optimization algorithm (Kaveh and Khayatazad, 2012), dolphin echolocation (Kaveh and Farhoudi, 2013), symbiotic organisms search (SOS) (Cheng and Prayogo, 2014), dragonfly algorithm (Mirjalili, 2016), Jaya algorithm (Rao, 2016), butterfly optimization algorithm (Qi et al, 2017), thermal txchange optimization (Kaveh and Dadras, 2017), focus group algorithm (Fattahi et al, 2018), squirrel search algorithm (Jain et al, 2019), Blue Monkey algorithm (Mahmood and Al-Khateeb, 2019), booster algorithm (Pakzad-Moghaddam et al, 2019), salmon migration algorithm (Deng and Zhu, 2019), sailfish optimizer algorithm (Shadravan, 2019), bear smell search algorithm (Ghasemi-Marzbali, 2020), most valuable player Algorithm (Bouchekara, 2020), and Newton MH algorithm (Gholizadeh et al, 2020). There are also numerous improved, modified, enhanced, and hybrid MH algorithms which improve on the above basic algorithms…”

Section: Introductionmentioning

confidence: 99%

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

Makiabadi

Maheri

2021

Advances in Structural Engineering

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An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

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A novel optimization booster algorithm

Cited by 18 publications

References 62 publications

A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design

A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design

Optimal Design of Truss Using a Hybrid Method Based on Particle Swarm Optimizer and Harris Hawk Algorithm

An enhanced symbiotic organisms search algorithm for design optimization of trusses with frequency constraints

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