Infeasibility Driven Evolutionary Algorithm Research Articles

Modelling and Optimization of Phase Locked Loop under Constrained Channel Length and Width of MOSFETs

CMOS integrated circuits consisting of MOSFETs have tradeoffs among their performance parameters. Hence they need minimization in those tradeoffs calling for multi objective optimization to yield a circuit with enhanced characteristics. To perform simultaneous optimization of the Phase locked loop (PLL) performances using an effective multi objective optimization technique saving the designer’s time and causing the near best performance is the motivation of this work. Though the designer can optimize the circuit in the netlist level, it is less effective and a time consuming iterative process and sometimes it is next to impossible for complex and nanoscale circuits with large number of MOSFET devices and interconnects. Performance parameters like phase noise, lock time and power consumption are optimized subject to the practical design constraints using an efficient multi-objective optimization technique, infeasibility driven evolutionary algorithm (IDEA) in a real time environment. Using design parameters like the channel length and width of the MOSFETs for optimal performance, the PLL is simulated for model validation. Significantly superior performance achieved by the designed PLL is demonstrated. The phase noise, average power consumption and lock time achieved here are −126.3 dBc/Hz at 1 MHz offset frequency, 1.523 mW and 50 nS respectively.

Feasibility-Preserving Genetic Operators for Hybrid Algorithms using TSP solvers for the Inventory Routing Problem

In this paper feasibility-preserving genetic operators for hybrid algorithms using TSP solvers for the Inventory Routing Problem (IRP) are studied. The IRP is a problem of jointly optimizing delivery schedules and routes for vehicles transporting products from a supplier to a number of retailers. This optimization problem is highly constrained, because limits on inventory levels as well as the vehicle capacity have to be taken into account. Moreover, the IRP is a generalization of the TSP and solving the TSP effectively is an important part of obtaining good solutions to the IRP. In this paper evolutionary algorithms are used for solving the IRP, but finding good routes is delegated to a state-of-the-art TSP solver. Therefore, genetic operators used in this paper focus on constructing the delivery schedule and not on optimizing the routes. In the experimental part of the paper an evolutionary algorithm using feasibility-preserving genetic operators is compared to the Infeasibility Driven Evolutionary Algorithm (IDEA) that uses the selective pressure to obtain feasible solutions. Presented results suggest that designing good feasibility-preserving genetic operators is important, because allowing the optimization algorithm to generate infeasible solutions and handling infeasibility in IDEA using the selective pressure leads to inferior results.

Efficient Surrogate-Based NVH Optimization of a Full Vehicle Using FRF Based Substructuring

<div class="section abstract"><div class="htmlview paragraph">The computer simulation with the Finite Element (FE) code for the structural dynamics becomes more attractive in the industry. However, it normally takes a prohibitive amount of computation time when design optimization is performed with running a large-scale FE simulation many times. Exploiting Dynamic Structuring (DS) leads to alleviating the computational complexity since DS necessities iterative reanalysis of only the substructure(s) to be optimally designed. In this research, Frequency Response Function (FRF) based substructuring is implemented to realize the benefits of DS for fast single- and multi-objective evolutionary design optimization. Also, Differential Evolution (DE) is first combined with two sorting approaches of Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Infeasibility Driven Evolutionary Algorithm (IDEA) for effective constrained single- and multi-objective evolutionary optimization. The effectiveness of the proposed algorithm (NSGA-II/DE-IDEA) is verified using several test functions for constrained single- and multi-objective optimization. To circumvent the need for frequent time-consuming simulation runs, Kriging surrogate models are established by interpolating the responses simulated at the sample points, which are generated by executing an Optimal LHS algorithm. Besides, the Morris method is implemented to leave out unimportant design variables. A constrained single-objective and a constrained multi-objective NVH design optimization of a truck are carried out to demonstrate the surrogate-based design optimization process involving FRF based substructuring and the proposed algorithm.</div></div>

Performance Measurement and Optimization of Relays Used for 5G Ultra Reliable Low Latency Communication Network

The objective of this paper is to analyse and optimize the performance parameters of Relay nodes used in the finite block length (FBL) regime. A relaying system with a single Decode and Forward (DF) Relay is used for this purpose. Here using FBL, the performance parameters like coding rate, decoding error probability etc are obtained for different scenarios like without relay, with relay and using cooperative relaying. Effects of SNR and code Block length on performance parameters are analyzed. To enhance the performance of the Relay in URLLC scenario, power distribution between source and Relay node is optimized using evolutionary algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) and Infeasibility Driven Evolutionary Algorithm (IDEA). Low error probability and high throughput at the desired block length and power were the optimization goals. After using both the algorithms, the optimized Relay has shown improvement in performances like throughput (coding rate) and decoding error probability. It is also observed that IDEA optimization approach is found to be more efficient than MOPSO to provide optimum design parameters.

Design and construction of an autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) are becoming increasingly popular for ocean exploration, military and industrial applications. In particular, AUVs are becoming an attractive option for underwater search and survey operations as they are inexpensive compared to manned vehicles. Previous attempts on AUV designs have focused primarily on functional designs while very little research has been directed to identify optimum designs. This paper presents an optimization framework for the design of AUVs using two state-of-the-art population based optimization algorithms, namely non-dominated sorting genetic algorithm (NSGA-II) and infeasibility driven evolutionary algorithm (IDEA). The framework is subsequently used to identify the optimal design of a torpedo-shaped AUV with an overall length of 1.3m. The preliminary design identified through the process of optimization is further analyzed with the help of a computer-aided design tool, CATIA to generate a detailed design. The detailed design has since then been built and is currently undergoing trials. The flexibility of the proposed framework and its ability to identify optimum preliminary designs of AUVs with different sets of user requirements are also demonstrated.

Design of optimal nano-CMOS differential VCO for RF applications

In the design of radio frequency (RF) circuits fast prototyping with optimal performance is a challenging task for designers. The noise consideration in the differential voltage controlled oscillator (DVCO) is very much vital in its design. The present work focuses on the design of low phase noise and low power robust nano-CMOS differential voltage controlled oscillator (DVCO) for a desired frequency of oscillation 2.4 GHz. Constrained multi-objective optimisation, infeasibility driven evolutionary algorithm (IDEA) is used to minimise the phase noise and power consumption simultaneously. In this work, the phase noise is formulated to inherently account for the flicker noise along with the thermal noise and optimised along with power consumption by IDEA. The optimal performing circuit is synthesised using GPDK-90 nm 1P9M process library. The frequency of oscillation obtained in the parasitic inclusive design of the differential VCO is 2.399513658 GHz which is in good agreement with the target frequency with negligible deviation and the corresponding optimum values of power consumption and phase noise recorded are 845.5095 µW and 79.67 dBc/Hz at 1 MHz offset, respectively.

A comparative study of Multi-Objective Ant Colony Optimization algorithms for the Time and Space Assembly Line Balancing Problem

Assembly lines for mass manufacturing incrementally build production items by performing tasks on them while flowing between workstations. The configuration of an assembly line consists of assigning tasks to different workstations in order to optimize its operation subject to certain constraints such as the precedence relationships between the tasks. The operation of an assembly line can be optimized by minimizing two conflicting objectives, namely the number of workstations and the physical area these require. This configuration problem is an instance of the TSALBP, which is commonly found in the automotive industry. It is a hard combinatorial optimization problem to which finding the optimum solution might be infeasible or even impossible, but finding a good solution is still of great value to managers configuring the line. We adapt eight different Multi-Objective Ant Colony Optimization (MOACO) algorithms and compare their performance on ten well-known problem instances to solve such a complex problem. Experiments under different modalities show that the commonly used heuristic functions deteriorate the performance of the algorithms in time-limited scenarios due to the added computational cost. Moreover, even neglecting such a cost, the algorithms achieve a better performance without such heuristic functions. The algorithms are ranked according to three multi-objective indicators and the differences between the top-4 are further reviewed using statistical significance tests. Additionally, these four best performing MOACO algorithms are favourably compared with the Infeasibility Driven Evolutionary Algorithm (IDEA) designed specifically for industrial optimization problems.

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.