Niched Pareto Genetic Algorithm Research Articles

Intelligent Optimization Approaches for a Secured Dynamic Partial Reconfigurable Architecture-Based Health Monitoring System

In this work, an intelligent multi-objective optimization technique is proposed to optimize various parameters such as power consumption ([Formula: see text]), computation time ([Formula: see text]) and area ([Formula: see text]) for a health monitoring system designed using the Secured Dynamic Partial Reconfiguration (SDPR) architecture. The cost-efficient Dynamic Partial Reconfiguration (DPR)-based field-programmable gate array (FPGA) is very useful for analyzing many applications such as automation and data processing. The novelty of this paper is to design a new intelligent SDPR (i-SDPR) module in order to achieve better performance in a health monitoring system by considering various performance parameters. The SDPR architecture is incorporated with two reconfigurable modules such as Encrypt and Authenticate, which results in an increase in the core power consumption ([Formula: see text]), computation time ([Formula: see text]) and area ([Formula: see text]). So, to improve the performance of the SDPR-based health monitoring system, there is a necessity to optimize the performance parameters and it is achieved through intelligent multi-objective evolutionary (MOEA) techniques. The intelligent multi-objective evolutionary techniques such as Niched-Pareto Genetic Algorithm (NPGA), Pareto Archived Evolution Strategy (PAES) and Pareto Envelope-based Selection Algorithm (PESA) have been considered for better optimization in the performance parameters. For the study, the free-to-use MIMIC-III dataset is taken, which contains critically admitted various intensive care unit patient data. The dataset is processed through any one of the multi-objective evolutionary operators till satisfying the conditions and then forwarded to implementation. The proposed architecture has been implemented and tested using Cyclone[Formula: see text] V SX SoC Development Kit. The comparative analysis of various performance parameters was done for the proposed i-SDPR with the existing techniques such as the DPR and SDPR approach to show the improvement. The results declare that the proposed techniques obtain better performance compared to the existing techniques.

Dimensional Synthesis for Multi-Linkage Robots Based on a Niched Pareto Genetic Algorithm

The dimensional synthesis of multi-linkage robots has great significance for improving flexibility and efficiency. With the increase of the degree of freedom and restrictions on special occasions, the solution of dimensional synthesis becomes complicated and time-consuming. Theory of workspace density function, maneuverability, and energy expenditure had been studied. With high flexibility and low energy consumption as the design goal, the method for dimensional and joint angle synthesis of multi-linkage robots was proposed based on a niched Pareto genetic algorithm. The Pareto solution set has been obtained. The method was verified by two application examples, which is occlusion of the solar salt evaporation pool and the secondary scattering of solid 2,2′-azobis(2,4-dimethylvaleronitrile). Through the application of NPGA (niched Pareto genetic algorithm) compared with KPCA (kernel principal component analysis), it can save 12.37% time in occlusion of one evaporating pool and reduce energy consumption by 3.85%; it can save 9.96% time in scattering of remain materials per barrel and reduce energy consumption by 1.77%. The study reduces the labor intensity of manual workers in the salt making industry, ensures the safe production of dangerous chemicals, and provides new ideas and methods for the dimensional synthesis of multi-linkage robots.

Multiobjective design optimization and analysis of magnetic flux distribution for slotless permanent magnet brushless DC motor using evolutionary algorithms

Abstract In this paper, Multi-Objective Optimization (MOO) techniques namely: Weighted Sum Method (WSM), Multi Objective Genetic Algorithm (MOGA) and Niched Pareto Genetic Algorithm (NPGA) are proposed for the design optimization and analysis of magnetic flux distribution for a slotless permanent magnet Brushless DC (BLDC) motor. The sensitivity analysis is carried out to identify the design variables of BLDC motor which influence the objective function. The objective functions are conflicting in nature (maximization of output torque and minimization of total volume and losses), hence, are optimized simultaneously by means of MOO algorithms. The proposed MOOs account for four kinds of design variables namely: rotor radius, stator/ rotor axial length, magnet thickness and winding thickness simultaneously to maximize the output torque and to reduce the total volume and total power loss. The conventional MOO such as WSM gives single pareto optimal solution. However, the proposed MOGA and NPGA algorithms create accurate and well-distributed pareto front set with few function evaluations. The performances of the three MOOs are evaluated and compared using the four performance metrics namely: Hyper Volume (HV), Generational Distance (GD), Inverted Generational Distance (IGD) and Spread. From the comparison, it is observed that NPGA gives better results. Using the design parameter obtained from NPGA, the magnetic flux distribution analysis of the BLDC motor is carried out to analyze variation of flux distribution in the different parts of the motor. The results thus obtained are compared with those obtained through Partial differential equation and single objective GA optimization. The detailed thermal analysis is also carried out to analyze the thermal behavior at different parts of the machine for different working conditions in the continuous operation mode and hence, the results obtained are compared with single objective GA optimization. The advantages of MOOs in the design optimization of slotless permanent magnet BLDC motor are highlighted.

Design Optimization of Heat Pipes Using Elitism-Based Self-Adaptive Multipopulation Jaya Algorithm

In this paper, an elitism-based self-adaptive multipopulation Jaya algorithm is proposed for the design optimization of heat pipes. The design of a heat pipe involves a number of geometric and physical parameters with high complexity. These processes are based on trial and error. General design approaches become tedious and time consuming. These processes do not guarantee the achievement of an optimal design. Therefore, metaheuristic-based computational methods are preferred. Hence, this study proposes a new optimization method for the design optimization of heat pipes using the elitism-based self-adaptive multipopulation Jaya algorithm. The search mechanism of the Jaya algorithm is enhanced in this work by using the multipopulation search scheme with elitism, which searches for the optimal solution in different search spaces of the problem simultaneously. This method is used for the single as well as multi-objective design optimizations of heat pipes. Minimization of the total mass of the heat pipe is considered as a single-objective problem with an array of working fluids, namely, methanol, ethanol, and ammonia. Furthermore, maximization of heat transfer rate and minimization of thermal resistance of a heat pipe are carried out simultaneously by using a priori approach. The results obtained by the elitism-based self-adaptive multipopulation Jaya algorithm are found superior as compared to the teaching-learning-based optimization, global best algorithm, Jaya, and self-adaptive multipopulation Jaya algorithms in the case of single-objective optimization, and in the case of multi-objective optimization it is found superior compared to the niched Pareto genetic algorithm, grenade-explosion method, teaching/learning-based optimization, Jaya, self-adaptive Jaya, and self-adaptive multipopulation Jaya algorithms.

Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm

The present study explores the use of an improved Jaya algorithm called self-adaptive Jaya algorithm for optimal design of selected thermal devices viz; heat pipe, cooling tower, honeycomb heat sink and thermo-acoustic prime mover. Four different optimization case studies of the selected thermal devices are presented. The researchers had attempted the same design problems in the past using niched pareto genetic algorithm (NPGA), response surface method (RSM), leap-frog optimization program with constraints (LFOPC) algorithm, teaching-learning based optimization (TLBO) algorithm, grenade explosion method (GEM) and multi-objective genetic algorithm (MOGA). The results achieved by using self-adaptive Jaya algorithm are compared with those achieved by using the NPGA, RSM, LFOPC, TLBO, GEM and MOGA algorithms. The self-adaptive Jaya algorithm is proved superior as compared to the other optimization methods in terms of the results, computational effort and function evalutions.

An Improved Multi-Objective Optimization Algorithm Based on NPGA for Cloud Task Scheduling

As a commercial distributed computing mode, cloud computing needs to meet the quality of service (QoS) requirement of users, which is its top priority. However, cloud computing service providers also need to consider how to reduce the overhead of data center, and keep load balancing is one of the key points to maximize the use of the resource in the data center. In this paper, we propose an improved multi-objective niched Pareto genetic algorithm (NPGA) to take load balancing into consideration without affecting performance of time consumption and financial cost of handling the user’s cloud computing tasks by presenting the load balancing shift mutation operator. The simulation results and analysis show that the proposed algorithm performs better than NPGA in maintaining the diversity and the distribution of the Pareto-optimal solutions in the cloud tasks scheduling under the same population size and evolution generation.

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.

Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty

Optimal design of long term groundwater monitoring (LTGM) network often involves conflicting objectives and substantial uncertainty arising from insufficient hydraulic conductivity (K) data. This study develops a new multi-objective simulation–optimization model involving four objectives: minimizations of (i) the total sampling costs for monitoring contaminant plume, (ii) mass estimation error, (iii) the first moment estimation error, and (iv) the second moment estimation error of the contaminant plume, for LTGM network design problems. Then a new probabilistic Pareto genetic algorithm (PPGA) coupled with the commonly used flow and transport codes, MODFLOW and MT3DMS, is developed to search for the Pareto-optimal solutions to the multi-objective LTGM problems under uncertainty of the K-fields. The PPGA integrates the niched Pareto genetic algorithm with probabilistic Pareto sorting scheme to deal with the uncertainty of objectives caused by the uncertain K-field. Also, the elitist selection strategy, the operation library and the Pareto solution set filter are conducted to improve the diversity and reliability of Pareto-optimal solutions by the PPGA. Furthermore, the sampling strategy of noisy genetic algorithm is adopted to cope with the uncertainty of the K-fields and improve the computational efficiency of the PPGA. In particular, Monte Carlo (MC) analysis is employed to evaluate the effectiveness of the proposed methodology in finding Pareto-optimal sampling network designs of LTGM systems through a two-dimensional hypothetical example and a three-dimensional field application in Indiana (USA). Comprehensive analysis demonstrates that the proposed PPGA can find Pareto optimal solutions with low variability and high reliability and is a promising tool for optimizing multi-objective LTGM network designs under uncertainty.

Multiobjective Construction Schedule Optimization Using Modified Niched Pareto Genetic Algorithm

AbstractA construction schedule must satisfy multiple project objectives that often conflict with each other. While several earlier approaches attempted to generate optimal schedules in terms of several criteria, most of their optimization processes were segmented into multiple steps. Owing to such a lack of simultaneous optimization, limited alternative solutions could be searched and some trade-offs between goals could not be identified. This paper presents an optimization approach that enables a simultaneous search for an optimal construction schedule in terms of three objectives: minimization of construction duration, cost, and resource fluctuation. A multiobjective optimization (MOO) approach was adopted to generate scheduling solutions considering all those objectives. To enable a simultaneous optimization, we propose a new data structure that can compute the performances of solutions in terms of all the objectives at the same time. A Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate ...

Optimal design of the heat pipe using TLBO (teaching–learning-based optimization) algorithm

Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (Teaching–Learning-Based Optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic Algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms.

Optimal design of groundwater remediation systems using a multi-objective fast harmony search algorithm

A new multi-objective optimization methodology is developed, whereby a multi-objective fast harmony search (MOFHS) is coupled with a groundwater flow and transport model to search for optimal design of groundwater remediation systems under general hydrogeological conditions. The MOFHS incorporates the niche technique into the previously improved fast harmony search and is enhanced by adding the Pareto solution set filter and an elite individual preservation strategy to guarantee uniformity and integrity of the Pareto front of multi-objective optimization problems. Also, the operation library of individual fitness is introduced to improve calculation speed. Moreover, the MOFHS is coupled with the commonly used flow and transport codes MODFLOW and MT3DMS, to search for optimal design of pump-and-treat systems, aiming at minimization of the remediation cost and minimization of the mass remaining in aquifers. Compared with three existing multi-objective optimization methods, including the improved niched Pareto genetic algorithm (INPGA), the non-dominated sorting genetic algorithm II (NSGAII), and the multi-objective harmony search (MOHS), the proposed methodology then demonstrated its applicability and efficiency through a two-dimensional hypothetical test problem and a three-dimensional field problem in Indiana (USA).

A novel multi-objective directed bee colony optimization algorithm for multi-objective emission constrained economic power dispatch

In this paper, a multi-objective directed bee colony optimization algorithm (MODBC) is comprehensively developed and successfully applied for solving a multi-objective problem of optimizing the conflicting economic dispatch and emission cost with both equality and inequality constraints is showcased. Classical optimization techniques like direct search and gradient methods fail to give the global optimum solution. The proposed algorithm is an integration of the deterministic search, the multi-agent system (MAS) environment and the bee decision-making process. Thus making use of deterministic search, multi-agent environment and bee swarms, the MODBC realizes the purpose of optimization. The hybridization makes MODBC to obtain a unique and fast solution and hence generate a better pareto front for multi-objective problems. The above mentioned multi-objective evolutionary algorithms have been applied to the standard IEEE 30 bus six generator test system. Results of the proposed algorithm have been compared with traditional methods like linear programming (LP) and multi-objective stochastic search technique (MOSST). The performance of the introduced algorithm is also compared with other evolutionary algorithms like Non-dominated Sorting Genetic Algorithm (NSGA), Niched Pareto Genetic Algorithm (NPGA) and Strength Pareto Evolutionary Algorithm (SPEA) and Particle Swarm Optimization (PSO). The results show the robustness and accuracy of the proposed algorithm over the traditional methods and its other multi-objective evolutionary algorithm (MOEA) counterparts.

A niched Pareto genetic algorithm for finding variable length regulatory motifs in DNA sequences

The transcription factor binding sites also called as motifs are short, recurring patterns in DNA sequences that are presumed to have a biological function. Identification of the motifs from the promoter region of the genes is an important and unsolved problem specifically in the eukaryotic genomes. In this paper, we present a niched Pareto genetic algorithm to identify the regulatory motifs. This approach is based on the maximization of two objectives of the problem that is the motif length and the consensus similarity score. A long motif means it is less likely to be a false motif. The similarity score represents a motifs probability of conservation in a given set of sequences. Proposed method can find multiple, variable length motifs. In this method, we represented a candidate motif as a combination of length and starting position of the motif in each sequence of the co-regulated genes. This enables the algorithm to identify multiple motifs of variable length. We applied this approach on various data sets and the results show that it can find multiple motifs of variable length in co-regulated genes.

Fuzzy Parallel Machines Scheduling Problem Based on Genetic Algorithm

A kind of unrelated parallel machines scheduling problem is discussed. The memberships of fuzzy due dates denote the grades of satisfaction with respect to completion times with jobs. Objectives of scheduling are to maximize the minimum grade of satisfaction while makespan is minimized in the meantime. Two kind of genetic algorithms are employed to search optimal solution set of the problem. Both Niched Pareto Genetic Algorithm (NPGA) and Nondominated Sorting Genetic Algorithm (NSGA-II) can find the Pareto optimal solutions. Numerical simulation illustrates that NSGA-II has better results than NPGA.

A MEM Electric Field Sensor Optimization by Multi-Objective Niched Pareto Genetic Algorithm

Micro-electro-mechanical systems (MEMS) have traditionally been optimized manually based on the solutions to dynamic equations and intuition. This paper presents the application of a multi-objective niched Pareto genetic algorithm (GA) to optimize a synthesized design of a MEM electric field sensor. The geometry of the sensor design is evolved in order to meet the objectives of maximal displacement of at least 5μm, minimal stress, minimal temperature and a resonant frequency near 2kHz. The algorithm gradually evolves a set of solutions towards a Pareto frontier in which no solution is better in all objectives than any other solution. When the algorithm has finished the designer may choose one or more solutions from the set that best meets their objectives given available trade-offs. The results show comparable or better performance in simulation than devices optimized manually or by other means.

Economic emission load dispatch through fuzzy based bacterial foraging algorithm

This paper presents a newly developed optimization approach involving a modified bacterial foraging algorithm (MBFA) applied for the solution of the economic and emission load dispatch (EELD) problem. The approach utilizes the natural selection of global optimum bacterium having successful foraging strategies in the fitness function. The bacterial foraging algorithm (BFA) appears to be a robust and reliable optimization algorithm for the solution of the EELD problems. To obtain the best compromising solution a fuzzy decision making approach using MBFA is applied to the standard IEEE 30-bus six generator test system and a Taiwan power system of 40 generating units with valve point loading effects. The results confirm the potential and effectiveness of the proposed algorithm compared to various methods such as, linear programming (LP), multi-objective stochastic search technique (MOSST), differential evolution (DE), non-dominated sorting genetic algorithm (NSGA), niched pareto genetic algorithm (NPGA), strength pareto evolutionary algorithm (SPEA) and fuzzy clustering based particle swarm optimization (FCPSO) performed in different central load dispatch centers to solve EELD problems. The quality and usefulness of the proposed algorithm is demonstrated through its application to two standard test systems in comparison with the other existing techniques. The current proposal was found to be better than, or at least comparable to them considering the quality of the solutions obtained.

Optimization of heat pipe with axial “Ω”-shaped micro grooves based on a niched Pareto genetic algorithm (NPGA)

A niched Pareto genetic algorithm based approach is utilized to optimize a heat pipe with axial “Ω”-shaped micro grooves. The effects of the structural parameters are evaluated and optimized with respect to the heat transfer performance in order to model the heat transfer capability and total thermal resistance of this novel heat pipe. Using the heat transfer capability and total thermal resistance as the objective function and the structure parameters as the decision variable, the optimization design for the heat pipe is performed using the niche genetic algorithm. The results indicate that the heat transfer capability and the total heat resistance are inversely coupled and as a result, the optimization must be constructed on the application objective. Using the niched Pareto genetic algorithm and the pre-specified application constraints, a Pareto-optimal solution set can be determined and the optimal design for a given application is selected.

Genetic-algorithm-based optimal apportionment of reliability and redundancy under multiple objectives

When solving multi-objective optimization problems subject to constraints in reliability-based design, it is desirable for the decision maker to have a sufficient number of solutions available for selection. However, many existing approaches either combine multiple objectives into a single objective or treat the objectives as penalties. This results in fewer optimal solutions than would be provided by a multi-objective approach. For such cases, a niched Pareto Genetic Algorithm (GA) may be a viable alternative. Unfortunately, it is often difficult to set penalty parameters that are required in these algorithms. In this paper, a multi-objective optimization algorithm is proposed that combines a niched Pareto GA with a constraint handling method that does not need penalty parameters. The proposed algorithm is based on Pareto tournament and equivalence sharing, and involves the following components: search for feasible solutions, selection of non-dominated solutions and maintenance of diversified solutions. It deals with multiple objectives by incorporating the concept of Pareto dominance in its selection operator while applying a niching pressure to spread the population along the Pareto frontier. To demonstrate the performance of the proposed algorithm, a test problem is presented and the solution distributions in three different generations of the algorithm are illustrated. The optimal solutions obtained with the proposed algorithm for a practical reliability problem are compared with those obtained by a single-objective optimization method, a multi-objective GA method, and a hybrid GA method.

Application of a niched Pareto genetic algorithm for selecting features for nuclear transients classification

Feature selection for transient classification is the problem of choosing among several monitored parameters (i.e., the features) to be used for efficiently recognizing the developing transient patterns. It is a critical issue for the application of “on condition” diagnostic techniques in complex systems, such as the nuclear power plants, where hundreds of parameters are measured. Indeed, irrelevant and noisy features have been shown to unnecessarily increase the complexity of the classification problem and degrade the diagnostic performance. In this paper, the problem of selecting the features to be used for efficient transient classification is tackled by means of multiobjective genetic algorithms. The approach leads to the identification of a family of equivalently optimal subsets of features, in the Pareto sense. However, difficulties in the convergence of the standard Pareto-based multiobjective genetic algorithm search in large feature spaces may arise in terms of representativeness of the identified Pareto front whose elements may turn out to be unevenly distributed in the objective functions space, thus not providing a full picture of the potential Pareto-optimal solutions. To overcome this problem, a niched Pareto genetic algorithm is embraced in this work. The performance of the feature subsets examined during the search is evaluated in terms of two optimization objectives: the classification accuracy of a Fuzzy K-Nearest Neighbors classifier and the number of features in the subsets. During the genetic search, the algorithm applies a controlled “niching pressure” to spread out the population in the search space so that convergence is shared on different niches of the Pareto front, which is thus evenly covered. The method is tested on a diagnostic problem characterized by a very large number of process features available for the classification of simulated transients in the feedwater system of a boiling water reactor. The dynamics of the transient signals is captured by wavelet decomposition, which actually increases the complexity of the search for the optimal feature subsets by triplicating the number of features to be considered. © 2008 Wiley Periodicals, Inc.

An Evolutionary Hybrid Scheduling Algorithm for Computational Grids

Grids promote user collaboration through flexible, coordinated sharing of distributed resources to solve a single large problem. Grid scheduling, similar to resource discovery and monitoring, is inherently more complex in Grid environments. We propose two approaches for solving Grid scheduling problems with the simultaneous objectives of maximizing the number of workflow executions and minimizing the waiting time variance among tasks of each workflow. One is the multiple objective Niched Pareto Genetic Algorithm (NPGA) that involves evolution during a comprehensive search and work on multiple solutions. After the Genetic search, we strengthen the search using Simulated Annealing as a local search meta-heuristic. For comparison, we evaluate other scheduling, such as, Tabu Search (TS), Simulated annealing (SA), and Discrete Particle Swarm Optimization (Discrete PSO). Results show that our proposed evolutionary Hybrid scheduling involving NPGA with an SA search, works better than other scheduling in considering workflow execution time within a deadline and waiting time variance in tasks with minimal iterations.