MOBA: Multi Objective Bat Algorithm for Combinatorial Optimization in VLSI

Laudis, Lalin L.; Shyam, Shilpa; Jemila, Caroline; Suresh, Vishnu

doi:10.1016/j.procs.2017.12.107

Cited by 22 publications

(9 citation statements)

References 14 publications

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“…Rather than scanning the whole quest space for Pareto ideal arrangements, decay based calculations break down a MOP into a lot of scalar improvement sub-problems by many weight vectors and the accomplishment of scalar punch work via an Achievement Scalar Function (ASF). Regularly utilized ASFs incorporate weighted total, Tchebycheff, vector edge separation scaling, and limit crossing point [7,22]. In the deterioration based methodology, since the ideal point related with each search course (weight vector) is focused on, adequate determination pressure advances can be given and furthermore a decent dispersion among arrangements can be kept up in a high-dimensional space.…”

Section: Decomposition Propertymentioning

confidence: 99%

“…The principal augmentation is a strategy that utilizes the current populace as contribution to produce a lot of objective vectors, and the subsequent one is to diminish the time unpredictability of wellness task of the first calculation. As related to the latter, the author consolidated the collection technique from MSOPS with the coordinated line search, dependent on approximated nearby inclination [7]. The proposed calculation has shown its seriousness on an obliged work with an inward Pareto front having up to 20 goals.…”

Section: Decomposition Propertymentioning

confidence: 99%

“…The pioneer chooses the section from the jam-packed fragments of the space arrangement and offers one of the non-prevailing arrangements. Choice is performed through the roulette wheel with the accompanying opportunities for each hypercube: (7) where is a steady number higher than 1 and is the quantity of acquired Pareto ideal arrangements in the portion. The condition demonstrates that the absence of blockage in the hypercube shows a high likelihood in the proposition of another pioneer.…”

Section: The Proposed Algorithm (Mobat/d)mentioning

confidence: 99%

“…At that point, we will apply it to take care of structure enhancement problems in building, for example, bi-objective shaft plan. In crafted by the paper [7] thought about MOBAT as an organic motivated meta-heuristic and have effectively applied it to take care of the difficult floor arranging in VSLI plan. A MOOP was proposed [8] to accomplish both of the referenced destinations.…”

Section: Introductionmentioning

confidence: 99%

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Using Multi-Objective Bat Algorithm for Solving Multi-Objective Non-linear Programming Problem

Sheah

Abbas

2021

eijs

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Human beings are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solutions to these problems. Nature acts as an optimized source for solving the complex problems. Decomposition is a basic strategy in traditional multi-objective optimization. However, it has not yet been widely used in multi-objective evolutionary optimization. Although computational strategies for taking care of Multi-objective Optimization Problems (MOPs) have been accessible for a long time, the ongoing utilization of Evolutionary Algorithm (EAs) to such issues gives a vehicle to tackle extremely enormous scope MOPs. MOBATD is a multi-objective bat algorithm that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the MOP by rewriting it as a set of Tchebycheff Approach, solving these problems simultaneously, within the BAT framework, might lead to premature convergence because of the leader selection process which uses the Tchebycheff Approach as a criterion. Dominance plays a major role in building the leaders archive, allowing the selected leaders to cover less dense regions while avoiding local optima and resulting in a more diverse approximated Pareto front. The results from 5 standard MOPs show that the MOBATD outperforms some developmental methods based on decomposition. All the results were achieved by MATLAB (R2017b).

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Section: Decomposition Propertymentioning

confidence: 99%

Section: Decomposition Propertymentioning

confidence: 99%

Section: The Proposed Algorithm (Mobat/d)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Using Multi-Objective Bat Algorithm for Solving Multi-Objective Non-linear Programming Problem

Sheah

Abbas

2021

eijs

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“…In addition, many scholars are committed to expanding the application fields of bat algorithms. For example, Laudis and other scholars have proposed a multi objective bat algorithm (MOBA) to solve the problem in Very Large-Scale Integration (VLSI) design [18]. Tawhid and Dsouza proposed a hybrid binary bat enhanced particle swarm optimization algorithm (HBBEPSO) to solve the feature selection problems [19].…”

Section: Introductionmentioning

confidence: 99%

An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors

Liu

et al. 2019

Symmetry

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This paper proposed an improved bat algorithm based on Lévy flights and adjustment factors (LAFBA). Dynamically decreasing inertia weight is added to the velocity update, which effectively balances the global and local search of the algorithm; the search strategy of Lévy flight is added to the position update, so that the algorithm maintains a good population diversity and the global search ability is improved; and the speed adjustment factor is added, which effectively improves the speed and accuracy of the algorithm. The proposed algorithm was then tested using 10 benchmark functions and 2 classical engineering design optimizations. The simulation results show that the LAFBA has stronger optimization performance and higher optimization efficiency than basic bat algorithm and other bio-inspired algorithms. Furthermore, the results of the real-world engineering problems demonstrate the superiority of LAFBA in solving challenging problems with constrained and unknown search spaces.

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Optimization of Placement and Routing Techniques: Congestion Estimation and Control in VLSI Design

Nagendra,

Manavalan

2024

Lecture Notes in Electrical Engineering

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MOBA: Multi Objective Bat Algorithm for Combinatorial Optimization in VLSI

Cited by 22 publications

References 14 publications

Using Multi-Objective Bat Algorithm for Solving Multi-Objective Non-linear Programming Problem

Using Multi-Objective Bat Algorithm for Solving Multi-Objective Non-linear Programming Problem

An Improved Bat Algorithm Based on Lévy Flights and Adjustment Factors

Optimization of Placement and Routing Techniques: Congestion Estimation and Control in VLSI Design

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