Refined Heuristic Swarm Intelligence Algorithm

Aina, Ibukun Isaac; Ejieji, C. N.

doi:10.34198/ejms.5221.267275

Cited by 2 publications

(4 citation statements)

References 4 publications

(4 reference statements)

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“…In recent years, heuristic swarm intelligence algorithms have demonstrated significant advantages in pipe layout [16], particularly the Artificial Fish Swarm Algorithm (AFSA) [17], which has shown superiority compared to other heuristic algorithms. Huang et al [18] successfully applied AFSA to robot path planning, while Zhang et al [19] utilized it in the path planning of Autonomous Surface ships.…”

Section: ) Research On Encoding Techniques and Optimization Algorithmsmentioning

confidence: 99%

“…For the crossover adjustment operation, a tournament method is used to select the best individuals for crossover, and a single-point crossover is applied based on the Equation (16). cross represents the function for performing crossover operations, with parameters including the best individuals of the entire population( best X ), the best individuals selected by the tournament method ( 1 best X ), and the crossover rate ( pcross ).…”

Section:  mentioning

confidence: 99%

“…The effectiveness of the proposed branch pipe collaborative layout method has been validated through a comparison with the layout methods and algorithm performance described in reference [41]. The layout space of this case study is a cube with dimensions approximately 40 in length, width, and height, consisting of six obstacles (ship equipment) with their respective diagonal vertex coordinates as follows: [{(6, 12, 1), (24,16,40)}O1, {(32, 12, 1), (40,16,40)}O2, {(1, 26, 1), (8,30,40)}O3, { (16,26,1), (34,30,40)}O4, {(1, 1, 12), (24,40,16)}O5, { (16,1,26), (40,40,30)}O6]. There are four interfaces with the following coordinates in this case: (20,40,20), (20,1,1), (1,40,40), and (40,20,30).…”

Section: B Case 2: Verification and Analysis Of Branch Pipe Collabora...mentioning

confidence: 99%

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Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Zhang,

Yang,

Yang

et al. 2023

IEEE Access

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Ship Pipe Route Design (SPRD) has always been a challenging and critical aspect in the detailed design for various types of ships. However, existing layout methods are inefficient, existing algorithms lack consideration for collaborative layouts in practical engineering, true intelligent design has not yet been achieved, and the issue of collaborative layout of pipes has been insufficiently researched. Based on the aforementioned research status, this paper proposes a novel collaborative pipe layout method. Firstly, a high-dimensional vector encoding technique to innovate the underlying mathematical representation of pipes is proposed, breaking free from traditional constraints. Secondly, a powerful and engineering-applicable hybrid AA-SPOP algorithm is incorporated, which is based on the heuristic swarm algorithm known as Artificial Fish Swarm Algorithm (AFSA), exhibits advantages such as high global optimization efficiency and strong adaptability. To mitigate potential issues like suboptimal local optimization and high sensitivity, this paper proposes a parameter adaptive equation and a series of optimization adjustment. Thirdly, a collaborative pipe layout method that considers support equipment is proposed. This paper effectively addresses the lack of research in this area by introducing branch pipe collaboration methods and energy zone guidance methods. Finally, through engineering simulation experiments, the paper demonstrates the efficiency, feasibility, and cutting-edge nature of the proposed collaborative layout optimization method.INDEX TERMS Heuristic hybrid algorithm, ship pipe and equipment collaborative layout optimization, Ship Pipe Route Design (SPRD), vector encoding technique.

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Section: ) Research On Encoding Techniques and Optimization Algorithmsmentioning

confidence: 99%

Section:  mentioning

confidence: 99%

Section: B Case 2: Verification and Analysis Of Branch Pipe Collabora...mentioning

confidence: 99%

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Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Zhang,

Yang,

Yang

et al. 2023

IEEE Access

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“…This was done by introducing the memory mechanism, new selection strategy and depuration procedure. Other works on modified cuckoo search and some other metaheuritic algorithms can be found in [5], [6], [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Cuckoo Intelligence Search Algorithm

Aina¹,

Peter²,

Ayoade³

et al. 2021

International Journal of Difference Equations

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Cuckoo Search (CS) algorithm is a meta-heuristic technique that displays several merits. For example, it is easier to apply and less tuning parameters also, it is suitable for solving optimization problems. However, easily fall into local optimum has been established and has a slow convergence rate as a result of the cuckoo search parameters being kept constant. Therefore to handle this issue, an Enhanced Cuckoo Intelligence Search (ECIS) algorithm was developed which is an upgraded CS algorithm. The efficiency of ECIS was tested by some benchmark constrained optimization test functions and it was shown that ECIS gives a better optimal value than CS.

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Refined Heuristic Swarm Intelligence Algorithm

Cited by 2 publications

References 4 publications

Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Collaborative Layout Optimization for Ship Pipes Based on Spatial Vector Coding Technique

Enhanced Cuckoo Intelligence Search Algorithm

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