A Lion’s Pride Inspired Algorithm for VLSI Floorplanning

The complexity of any integrated circuit pushes the researchers to optimize the various parameters in the design process. Usually, the Nondeterministic Polynomial problems in the design process of Very Large Scale Integration (VLSI) are considered as a Single Objective Optimization Problem (SOOP). However, due to the increasing demand for the multi-criterion optimization, researchers delve up on Multi-Objective Optimization methodologies to solve a problem with multiple objectives. Moreover, it is evident from the literature that biologically inspired algorithm works very well in optimizing a Multi-Objective Optimization Problem (MOOP). This paper proposes a new Lion’s pride inspired algorithm to solve any MOOP. The methodologies mimic the traits of a Lion which always strives to become the Pride Lion. The Algorithm was tested with VLSI floorplanning problem wherein the area and dead space are the objectives. The algorithm was also tested with several standard test problems. The tabulated results justify the ruggedness of the proposed algorithm in solving any MOOP.

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Improved NSGAII with a New Distribution and its Application in Multi-Objective Reservoir Operation

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.90-93.2734 ◽

2011 ◽

Vol 90-93 ◽

pp. 2734-2739

Author(s):

Ruan Yun ◽

Cui Song Yu

Keyword(s):

Genetic Algorithms ◽

Control Strategy ◽

Reservoir Operation ◽

Large Scale ◽

Pareto Front ◽

Optimization Problem ◽

Shape Parameter ◽

Multi Objective Optimization ◽

Multi Objective ◽

New Distribution

Non-dominated sorting genetic algorithms II (NSGAII) has been widely used for multi- objective optimizations. To overcome its premature shortcoming, an improved NSGAII with a new distribution was proposed in this paper. Comparative to NSGAII, improved NSGAII uses an elitist control strategy to protect its lateral diversity among current non-dominated fronts. To implement elitist control strategy, a new distribution (called dogmatic distribution) was proposed. For ordinary multi-objective optimization problem (MOP), an ordinary exploration ability of improved NSGAII should be maintained by using a larger shape parameter r; while for larger-scale complex MOP, a larger exploration ability of improved NSGAII should be maintained by using a less shape parameter r. The application of improved NSGAII in multi-objective operation of Wohu reservoir shows that improved NSGAII has advantages over NSGAII to get better Pareto front especially for large-scale complex multi-objective reservoir operation problems.

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Design and Assembly of DNA Molecules Using Multi-Objective Optimisation

Synthetic Biology ◽

10.1093/synbio/ysab026 ◽

2021 ◽

Author(s):

Angelo Gaeta ◽

Valentin Zulkower ◽

Giovanni Stracquadanio

Keyword(s):

Synthetic Biology ◽

Design Process ◽

Optimization Problem ◽

Manufacturing Constraints ◽

Multi Objective Optimization ◽

Dna Molecules ◽

Multi Objective ◽

Rational Engineering ◽

Dna Engineering ◽

Design Requirements

Abstract Rapid engineering of biological systems is currently hindered by limited integration of manufacturing constraints into the design process, ultimately reducing the yield of many synthetic biology workflows. Here we tackle DNA engineering as a multi-objective optimization problem aiming at finding the best tradeoff between design requirements and manufacturing constraints. We developed a new open-source algorithm for DNA engineering, called Multi-Objective Optimisation algorithm for DNA Design and Assembly (MOODA), available as a Python and Anaconda package, as well as a Docker image. Experimental results show that our method provides near optimal constructs and scales linearly with design complexity, effectively paving the way to rational engineering of DNA molecules from genes to genomes.

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An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism

Applied Computational Intelligence and Soft Computing ◽

10.1155/2012/682372 ◽

2012 ◽

Vol 2012 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Yufang Qin ◽

Junzhong Ji ◽

Chunnian Liu

Keyword(s):

Evolutionary Algorithm ◽

Pareto Front ◽

Optimization Problem ◽

State Of The Art ◽

Early Stage ◽

Test Problems ◽

Multiobjective Optimization Problem ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective

Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the population. Specifically, it accelerates the population to approach the true Pareto front at the early stage of the evolution process. A strategy based on entropy is used to maintain the diversity of population when the population is near to the Pareto front. The proposed algorithm is executed on widely used test problems, and the simulated results show that the algorithm has better or comparative performances in convergence and diversity of solutions compared with two state-of-the-art evolutionary algorithms: NSGA-II, SPEA2 and the MOSADE.

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An Efficient Framework Using Normalized Dominance Operator for Multi-Objective Evolutionary Algorithms

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2019010102 ◽

2019 ◽

Vol 10 (1) ◽

pp. 15-37 ◽

Cited By ~ 1

Author(s):

Muneendra Ojha ◽

Krishna Pratap Singh ◽

Pavan Chakraborty ◽

Shekhar Verma

Keyword(s):

Evolutionary Algorithms ◽

Pareto Front ◽

Optimization Problem ◽

Computational Cost ◽

Optimization Methods ◽

Objective Test ◽

Test Problems ◽

Multi Objective Optimization ◽

Pareto Dominance ◽

Multi Objective

Multi-objective optimization algorithms using evolutionary optimization methods have shown strength in solving various problems using several techniques for producing uniformly distributed set of solutions. In this article, a framework is presented to solve the multi-objective optimization problem which implements a novel normalized dominance operator (ND) with the Pareto dominance concept. The proposed method has a lesser computational cost as compared to crowding-distance-based algorithms and better convergence. A parallel external elitist archive is used which enhances spread of solutions across the Pareto front. The proposed algorithm is applied to a number of benchmark multi-objective test problems with up to 10 objectives and compared with widely accepted aggregation-based techniques. Experiments produce a consistently good performance when applied to different recombination operators. Results have further been compared with other established methods to prove effective convergence and scalability.

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Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

Discrete Dynamics in Nature and Society ◽

10.1155/2011/569784 ◽

2011 ◽

Vol 2011 ◽

pp. 1-37 ◽

Cited By ~ 38

Author(s):

Wenping Zou ◽

Yunlong Zhu ◽

Hanning Chen ◽

Beiwei Zhang

Keyword(s):

Multiobjective Optimization ◽

Artificial Bee Colony ◽

Optimization Problems ◽

Difficult Problem ◽

Standard Test ◽

Test Problems ◽

Multi Objective Optimization ◽

Multi Objective ◽

Flight Direction ◽

Bee Colony

Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC) to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

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Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽

10.3390/s21082775 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2775

Author(s):

Tsubasa Takano ◽

Takumi Nakane ◽

Takuya Akashi ◽

Chao Zhang

Keyword(s):

Optimization Problem ◽

Line Pair ◽

Objective Functions ◽

Multi Objective Optimization ◽

Multi Objective ◽

Optimization Framework ◽

Visually Impaired People ◽

Comprehensive Comparison ◽

Basic Characteristics ◽

Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

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Auto-sharing parameters for transfer learning based on multi-objective optimization

Integrated Computer-Aided Engineering ◽

10.3233/ica-210655 ◽

2021 ◽

pp. 1-13

Author(s):

Hailin Liu ◽

Fangqing Gu ◽

Zixian Lin

Keyword(s):

Transfer Learning ◽

Optimization Problem ◽

Data Sets ◽

Multi Objective Optimization ◽

Particle Swarm Optimizer ◽

Real World Data ◽

Data Set ◽

Target Task ◽

Main Research ◽

Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

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