Optimal design of high-rise building wiring based on ant colony optimization

Liu, Chunjiang

doi:10.1007/s10586-018-2195-y

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…18 correspond to the designs given in Figs. 17 In this study, we demonstrate how the proposed framework also supports multi-loop optimization problems and can be used for more complex interconnected component layouts. A two-loop system optimization result is shown in Fig.…”

Section: Case Study 2: Bi-objective Optimization Problemmentioning

confidence: 90%

“…Especially in the electrical engineering domain, many examples of 2D routing algorithms were developed for VLSI circuit layouts 1 based on Manhattan rules and its variants [13]. Some other applications include aero-engine routing [14], ship pipe routing [15], chemical plant pipe routing [16], electrical wire routing in buildings [17], in developing CAD-based FPGA (field-programmable gate array) design tools [18], unmanned aerial vehicle navigation [19], and robotic path planning [20]. Optimization approaches have incorporated metrics such as packaging volume and mass properties [21], and have utilized solution methods such as simulated annealing [22,23], pattern search [24,25], genetic algorithms [26], ant colony optimization [27], and several other heuristic methods [28].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Two-Stage Design Framework for Two-Dimensional Spatial Packing of Interconnected Components

Peddada

James

Allison

2020

Journal of Mechanical Design

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Packing and routing separately are each challenging NP-hard problems. Therefore, solving the coupled packing and routing problem simultaneously will require disruptive methods to better address pressing related challenges, such as system volume reduction, interconnect length reduction, ensuring non-intersection, and physics (thermal, hydraulic, or electromagnetic) considerations. Here we present a novel two-stage sequential design framework to perform simultaneous physics-based packing and routing optimization. Stage 1 generates interference-free initial layouts that are fed to stage 2 as starting points to perform continuous physics-based optimization. Three distinct strategies for stage 1 have been introduced recently, 1) the force-directed layout method (FDLM), 2) an extension of the shortest path algorithms (SPAs), and 3) a unique geometric topology (UGT) generation algorithm. In stage 2, a gradient-based topology optimization method is used to simultaneously optimize both component locations and interconnect routing paths. In addition to geometric considerations, this method supports optimization based on system behavior by including physics-based objectives and constraints. The proposed framework is demonstrated using three case studies. First, the layout generation methods developed for stage 1 are compared with respect to system performance metrics obtained from stage 2. Second, a multi-objective optimization problem using the epsilon-constraint method is solved to obtain Pareto optimal solutions. Third, extension to multi-loop systems is demonstrated. In summary, the design automation framework integrates several elements together as a step toward a more comprehensive solution of 3D packing and routing problems with both geometric and physics considerations.

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Section: Case Study 2: Bi-objective Optimization Problemmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

A Novel Two-Stage Design Framework for Two-Dimensional Spatial Packing of Interconnected Components

Peddada

James

Allison

2020

Journal of Mechanical Design

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“…In response to this problem, many scholars have designed various intelligent algorithms inspired by biological and physical phenomena in nature and the behavior of animal groups. Tese algorithms include the particle swarm optimization (PSO) algorithm [1], ant colony optimization algorithm (ACO) [2], diferential evolution (DE) algorithm [3], frefy algorithm (FA) [4], bat algorithm (BA) [5], grey wolf optimization (GWO) [6], gravitational search algorithm (GSA) [7], freworks algorithm (FWA) [8], sine cosine algorithm (SCA) [9], naked mole-rat (NMR) algorithm [10], slime mould algorithm (SMA) [11], farmland fertility algorithm (FFA) [12], Harris hawks optimization (HHO) algorithm [13], cuckoo search optimization (CSO) algorithm [14], sparrow search algorithm (SSA) [15], ant lion optimizer (ALO) algorithm [16], African vultures optimization algorithm (AVOA) [17], mountain gazelle optimizer (MGO) [18], artifcial gorilla troops optimizer (GTO) [19], improved gorilla troops optimizer (IGTO) [20], improved hybrid aquila optimizer and African vultures optimization algorithm (IHAOAVOA) [21], and enhanced honey badger algorithm (EHBA) [22]. Tey provide powerful tools for the optimal solution of complex functions.…”

Section: Introductionmentioning

confidence: 99%

CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

Hsieh,

Zhang,

et al. 2023

Discrete Dynamics in Nature and Society

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This paper proposes an improved whale optimization algorithm with chaotic mapping and adaptive iteration strategy (CMAIS-WOA). This algorithm addresses the issues of the WOA algorithm that is prone to local optimal solutions with low stability. CMAIS-WOA utilizes chaotic mapping to enhance the diversity and coverage of the initial population. Also, it adaptively adjusts the weight values based on the current distribution of whale populations and the fitness of search agents. In addition, CMAIS-WOA uses an improved nonlinear convergence factor to adjust the breadth-first and depth-first search during the optimization process. The performance of the proposed CMAIS-WOA is evaluated by using 13 classical benchmark functions and IEEE CEC2014 test suite. The experimental results show that CMAIS-WOA effectively improves the stability of the optimal solution and helps the algorithm to approach the global optimal solution. The method proposed in this paper contributes to the field of optimization which solves problems more powerfully and efficiently.

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