Hybrid Pelican Komodo Algorithm

Kusuma, Purba Daru; Dinimaharawati, Ashri

doi:10.14569/ijacsa.2022.0130607

Cited by 5 publications

(10 citation statements)

References 24 publications

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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%

“…SMA is chosen because it represents metaheuristic that implements segregation of roles [27]. HPKA is chosen because it represents a new metaheuristic that hybridizes two existing metaheuristics [24]. GSO is chosen because it represents a new version of metaheuristic that combines the global best solution and local best solution for its reference in the guided search [33], which is firstly introduced in PSO.…”

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%

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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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Section: Simulation and Resultsmentioning

confidence: 99%

Section: Simulation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2023

IJIES

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“…The circumstances of these problems are also various, such as ample solution space, a massive number of decision variables or dimension, non-convex problems, multiple objectives, and ambiguous [25]. Third, many new metaheuristics are developed by modifying the existing ones or by combining several of them, such as the hybrid pelican Komodo algorithm (HPKA) [38], stochastic Komodo algorithm (SKA) [39], and other similar approaches.…”

Section: Introductionmentioning

confidence: 99%

Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

2023

IJIES

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This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO). As the name suggests, ALO deploys two strategies to find the optimal solution. The central concept of ALO is to intensify guided searches as a required method. Then, a random search is performed only if the guided search fails to improve the current solution. ALO consists of four guided searches and one random search, performed in three phases: two mandatory and one optional. In the first phase, the guided search is conducted with the best global solution as the reference. In the second phase, the guided search is conducted with a randomly selected solution as the reference. The random search is performed in the third phase. Evaluating ALO, it was tested on 23 classic functions and benchmarked against five existing metaheuristics with known shortcomings: Mixed leader-based optimizer (MLBO), slime mould algorithm (SMA), golden search optimizer (GSO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The results indicate that ALO is highly competitive, outperforming MLBO, SMA, GSO, COA, and ZOA in solving 16, 16, 14, 10, and 9 functions respectively, and demonstrating ALO as a promising new metaheuristic.

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“…The examples of them are tunicate swarm algorithm (TSA) [23], golden search optimization (GSO) [24], election-based optimization algorithm (EBOA) [25], hybrid leader based optimization (HLBO) [26], northern goshawk optimization (NGO) [27], butterfly optimization algorithm (BOA) [28], Komodo mlipir algorithm (KMA) [29], mixed leader based optimizer (MLBO) [30], multileader optimizer (MLO) [31], pelican optimization algorithm (POA) [32], and so on. Several new algorithms were built by modifying the previous swarm-based metaheuristic, such as the chaotic slime mold algorithm (CSMA) [33], stochastic Komodo algorithm (SKA) [34], modified honey badger algorithm (MHGA) [35], hybrid pelican Komodo algorithm (HPKA) [36], and many others.…”

Section: Introductionmentioning

confidence: 99%

Best-worst northern goshawk optimizer: a new stochastic optimization method

Kusuma,

Hasibuan

2023

IJECE

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<span lang="EN-US">This study introduces a new metaheuristic method: the best-worst northern goshawk optimizer (BW-NGO). This algorithm is an enhanced version of the northern goshawk optimizer (NGO). Every BW-NGO iteration consists of four phases. First, each agent advances toward the best agent and away from the worst agent. Second, each agent moves relatively to the agent selected at random. Third, each agent conducts a local search. Fourth, each agent traces the space at random. The first three phases are mandatory, while the fourth phase is optional. Simulation is performed to assess the performance of BW-NGO. In this simulation, BW-NGO is confronted with four algorithms: particle swarm optimization (PSO), pelican optimization algorithm (POA), golden search optimizer (GSO), and northern goshawk optimizer (NGO). The result exhibits that BW-NGO discovers an acceptable solution for the 23 benchmark functions. BW-NGO is better than PSO, POA, GSO, and NGO in consecutively optimizing 22, 20, 15, and 11 functions. BW-NGO can discover the global optimal solution for three functions.</span>

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Hybrid Pelican Komodo Algorithm

Cited by 5 publications

References 24 publications

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

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

Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

Best-worst northern goshawk optimizer: a new stochastic optimization method

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