Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems

Dehghani, Mohammad; Hubálovský, Štěpán; Trojovský, Pavel

doi:10.1109/access.2021.3133286

Cited by 247 publications

(154 citation statements)

References 36 publications

(31 reference statements)

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“…The strategy of pelicans in hunting and trapping prey was the main inspiration of Pelican Optimization Algorithm (POA) [36]. Some other swarm-based algorithms are: Cat and Mouse Based Optimizer (CMBO) [37], Good Bad Ugly Optimizer (GBUO) [38], Marine Predator Algorithm (MPA) [39], Tasmanian Devil Optimization (TDO) [40], Mutated Leader Algorithm (MLA) [41], Tunicate Search Algorithm (TSA) [42], Northern Goshawk Optimization (NGO) [43], Donkey Theorem Optimizer (DTO) [44], Rat Swarm Optimization (RSO) [45], All Members Based Optimizer (AMBO) [46], Red Fox Optimization (RFO) [47], Best Members Based Optimizer (MBMBO) [48], and Mixed Leader Based Optimizer (MLBO) [49].…”

Section: Lecture Reviewmentioning

confidence: 99%

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Givi¹,

Hubálovská²

2023

Computers, Materials &Amp; Continua

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Metaheuristic algorithms are widely used in solving optimization problems. In this paper, a new metaheuristic algorithm called Skill Optimization Algorithm (SOA) is proposed to solve optimization problems. The fundamental inspiration in designing SOA is human efforts to acquire and improve skills. Various stages of SOA are mathematically modeled in two phases, including: (i) exploration, skill acquisition from experts and (ii) exploitation, skill improvement based on practice and individual effort. The efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal types. The optimization results show that SOA, by balancing exploration and exploitation, is able to provide good performance and appropriate solutions for optimization problems. In addition, the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed approach. Analysis and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achieves much more competitive results.

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Section: Lecture Reviewmentioning

confidence: 99%

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Givi¹,

Hubálovská²

2023

Computers, Materials &Amp; Continua

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“…In northern goshawk optimization (NGO), the leader is randomly selected among the solutions. Then, each solution conducts neighborhood search where the local search space is reduced linearly during the iteration [23]. In hybrid leader-based optimization (HLBO), a leader of each solution is constructed from the normalized quality of three solutions: the related solution, the highest quality solution, and the randomly selected solution [24].…”

Section: Related Workmentioning

confidence: 99%

Guided Pelican Algorithm

2022

IJIES

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This paper presented a new metaheuristic technique, namely the guided pelican algorithm (GPA). GPA has the improvements for a shortcoming algorithm, namely the pelican optimization algorithm (POA), that mimics the behaviour of pelican birds during hunting prey. It improves the original POA in three ways. First, GPA replaces the randomized target with the global best solution as a deterministic target in phase one. Second, GPA replaces the pelican's current location with the search space size in determining the local search space size in phase two. Third, GPA implements multiple candidates in both phases rather than a single candidate as it is used in the original POA. Simulation is implemented to observe GPA's performance in optimizing both theoretical and real-world problems. GPA is compared with four algorithms: marine predator algorithm (MPA), particle swarm optimization (PSO), komodo mlipir algorithm (KMA), and POA. The result shows that GPA outperforms all sparing algorithms in optimizing most benchmark functions. GPA is also implemented to optimize the portfolio problem. The result shows that GPA outperforms all sparing algorithms in optimizing the portfolio problem. It outperforms three sparing algorithms in optimizing the portfolio problem. Its performance is 9%, 11%, and 13%, better than PSO, MPA, and KMA consecutively. Meanwhile, its less than 1% worse than POA.

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“…In this part, ten benchmark functions are employed to run MSMA together with six other state-of-the-art algorithms including SMA [46], EO [47], TSA [48], JFA [49], NGOA [50], and BOA [51].…”

Section: Performance Of Msma For Ten Benchmark Functionsmentioning

confidence: 99%

“…e ideal of using the four best solutions is taken from equilibrium optimizer (EO) [47], and EO is also applied for comparison with MSMA. In addition to SMA and EO, other four algorithms including TSA [48], JFA [49], NGOA [50], and BOA [51] are also run for ten benchmark functions for comparisons. ese results indicate MSMA can be as good as or superior to these compared algorithms.…”

Section: Introductionmentioning

confidence: 99%

Finding Optimal Solutions for the Placement of Reactive and Active Power Generation Components in Distribution Networks Using a High-Performance Metaheuristic Algorithm

Kien

Tuyet

Phan

et al. 2022

International Transactions on Electrical Energy Systems

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This paper proposes a modified slime mold algorithm (MSMA) for finding optimal site and size of both photovoltaic generators (PGs) and shunt capacitors (SCs) in radial distribution power systems (RDPSs). The most suitable site together with the most effective size of PGs and SCs can reduce currents of conductors effectively, resulting in the significant reduction of active power loss on all conductors. MSMA and two other applied algorithms, slime mold algorithm (SMA) and equilibrium optimizer (EO), are implemented for three study cases of IEEE 33, 69, and 85 RDPSs including Case (1) place only PGs with both active and reactive power generations; Case (2) place both SCs and PGs with only active power generation; and Case (3) place both SCs and PGs with both active and reactive power generations. Comparisons with SMA and EO, MSMA can reach either the same loss or much smaller loss for study cases. Furthermore, MSMA also outperforms other previously published methods for the three study cases, and it can reach significantly smaller loss for the three cases by 1.5%, 30.55%, and 19.64% for system 1, 7.59%, 0.26%, and 0.32% for system 2, and 7.51%, 34.2%, and 22.65% for system 3. As comparing the loss among three study cases obtained by MSMA, Case 3 can reach less loss than Case 1 and Case 2 by 11.71% and 11.64% for system 1, 26.27% and 26.13% for system 2, and the same 26.6% for system 3. Thus, it is recommended that MSMA is the most effective algorithm and the placement of both SCs and PGs with active and reactive power generations is the most optimal.

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Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems

Cited by 247 publications

References 36 publications

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Skill Optimization Algorithm: A New Human-Based Metaheuristic Technique

Guided Pelican Algorithm

Finding Optimal Solutions for the Placement of Reactive and Active Power Generation Components in Distribution Networks Using a High-Performance Metaheuristic Algorithm

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