Guided Pelican Algorithm

doi:10.22266/ijies2022.1231.18

Cited by 32 publications

(49 citation statements)

References 28 publications

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“…On the other hand, limited interaction among agents in GPA makes TIA is superior to GPA in solving the high dimension unimodal functions and high dimension multimodal functions. This comparison also proofs that interaction between the corresponding agents and all other agents as carried out in TIA is more important than generating multiple candidates along the way between the corresponding agent and the global best solution as in GPA [12]. The result also indicates that TIA is powerful enough in finding the acceptable solutions in the low iteration and low population size.…”

Section: Discussionmentioning

confidence: 58%

“…The third cluster consists of ten fixed dimension multimodal functions. The detail description of the functions can be seen in [12] and [24]. In this research, the dimension for the high dimension functions is set to 20.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…In recent years, there are many studies proposing new swarm intelligence-based metaheuristics. Many metaheuristics were built based on the animal's behavior, such as emperor penguin colony (EPC) [4], red deer algorithm (RDA) [5], butterfly optimization algorithm (BOA) [6], Komodo mlipir algorithm (KMA) [7], stochastic Komodo algorithm (SKA) [8], squirrel search optimizer (SSO) [9], modified honey badger algorithm (MHBA) [10], northern goshawk optimizer (NGO) [11], guided pelican algorithm (GPA) [12], and so on. Some metaheuristics were built based on the human social activities, such as teaching learning-based optimizer (TLBO) [13], election-based optimization algorithm (EBOA) [14], driving training-based optimizer (DTBO) [15], modified social forces algorithm (MSF) [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

2023

IJIES

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This research introduced a novel simple metaheuristic called as total interaction algorithm (TIA). TIA is a swarm intelligence which relies on the interaction among solutions in the population. The core and distinct concept of TIA is that each solution interacts with all other solutions in every iteration to find the best possible solution. Then, this new alternative replaces the current solution if it is better than the current solution. In this research, TIA is tested to solve 23 functions that represent both unimodal and multimodal problems. TIA is benchmarked with five metaheuristics: particle swarm optimization (PSO), marine predator algorithm (MPA), golden search optimizer (GSO), guided pelican algorithm (GPA), and driving training-based optimizer (DTBO). The result indicates that TIA is superior to other benchmark metaheuristics, especially in solving the high dimension functions. TIA is better than PSO, MPA, GSO, GPA, and DTBO in 22,21,16,11,13 functions. The result also indicates that the increase of the maximum iteration improves the performance of TIA mostly in solving high dimension unimodal functions. Meanwhile, the increase of the population size is less significant to improve its performance. Overall, this research resumes that the interaction with as many as possible individuals is proven better than with only selected individuals as implemented in many other metaheuristics.

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Section: Discussionmentioning

confidence: 58%

Section: Simulation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

2023

IJIES

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“…There are seven high dimensional unimodal functions in the first group (Sphere, Schwefel 2. In this work, QTO is benchmarked with five other shortcoming metaheuristics: MPA [18], SMA [27], GSO [33], HPKA [24], and GPA [25]. The reason of choosing these five metaheuristics is as follow.…”

Section: Simulation and Resultsmentioning

confidence: 99%

“…Most of these shortcoming metaheuristics used metaphors for its name. Some metaheuristics adopt animal as metaphors, such as cheetah optimizer (CO) [17], marine predator algorithm (MPA) [18], butterfly optimization algorithm (BOA) [19], racoon optimization algorithm (ROA) [20], northern goshawk optimizer (NGO) [21], pelican optimization algorithm (POA) [22], Komodo mlipir algorithm (KMA) [23], guided pelican Komodo algorithm (HPKA) [24], guided pelican algorithm (GPA) [25], tunicate swarm algorithm (TSA) [26], and so on. Some metaheuristics used International Journal of Intelligent Engineering and Systems, Vol.…”

Section: Introductionmentioning

confidence: 99%

Quad Tournament Optimizer: A Novel Metaheuristic Based on Tournament Among Four Strategies

2023

IJIES

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This paper introduced a novel metaheuristic that is developed based on the tournament mechanism, namely quad tournament optimizer (QTO). As its name suggests, QTO proposes a new approach of metaheuristic in which there are four searches conducted by each agent in every iteration. These searches are: (1) searching toward the global best solution, (2) searching toward the middle between the global best solution and a randomly selected solution, (3) searching relative to a randomly selected solution, and (4) neighbourhood search around the corresponding solution and the global best solution. A solution candidate is generated by each search. Then, a tournament is carried out to find the best candidate. This strategy is novel because most of metaheuristic deploys only single search or multiple searches where each search is conducted sequentially. QTO is challenged to find the optimal solution of 23 classic functions. In this challenge, QTO is benchmarked against five shortcoming metaheuristics: marine predator algorithm (MPA), slime mould algorithm (SMA), golden search optimizer (GSO), hybrid pelican Komodo algorithm (HPKA), and guided pelican algorithm (GPA). The result indicates that QTO outperforms all these benchmark metaheuristics.

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Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance

Kusuma¹,

Dinimaharawati²

2023

IJACSA

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Adaptability becomes important in developing metaheuristic algorithms, especially in tackling stagnation. Unfortunately, almost all metaheuristics are not equipped with an adaptive approach that makes them change their strategy when stagnation happens during iteration. Based on this consideration, a new metaheuristic, called an adaptive balance optimizer (ABO), is proposed in this paper. ABO's unique strategy focuses on exploitation when improvement happens and switching to exploration during stagnation. ABO also uses a balanced strategy between exploration and exploitation by performing two sequential searches, whatever circumstance it faces. These sequential searches consist of one guided search and one random search. Moreover, ABO also deploys both a strict acceptance approach and a non-strict acceptance approach. In this work, ABO is challenged to solve a set of 23 classic functions as a theoretical optimization problem and a portfolio optimization problem as the use case for the practical optimization problem. In portfolio optimization, ABO should optimize the quantity of ten stocks in the energy and mining sector listed in the IDX30 index. In this evaluation, ABO is competed with five other metaheuristics: marine predator algorithm (MPA), golden search optimizer (GSO), slime mold algorithm (SMA), northern goshawk optimizer (NGO), and zebra optimization algorithm (ZOA). The simulation result shows that ABO is better than MPA, GSO, SMA, NGO, and ZOA in solving 21, 18, 16, 11, and 8, respectively, in solving 23 functions. Meanwhile, ABO becomes the third-best performer in solving the portfolio optimization problem.

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Guided Pelican Algorithm

Cited by 32 publications

References 28 publications

Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

Total Interaction Algorithm: A Metaheuristic in which Each Agent Interacts with All Other Agents

Quad Tournament Optimizer: A Novel Metaheuristic Based on Tournament Among Four Strategies

Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance

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