Multi-objective Particle Research Articles

Stochastic modeling and meta-heuristic multivariate optimization of bioprocess conditions for co-valorization of feather and waste frying oil toward prodigiosin production

Serratia marcescens strain UCCM 00009 produced a mixture of gelatinase and keratinase to facilitate feather degradation but concomitant production of prodigiosin could make waste feather valorization biotechnologically more attractive. This article describes prodigiosin fermentation through co-valorization of waste feather and waste frying peanut oil by S. marcescens UCCM 00009 for anticancer, antioxidant, and esthetic applications. The stochastic conditions for waste feather degradation (WFD), modeled by multi-objective particle swarm-embedded-neural network optimization (ANN-PSO), revealed a gelatinase/keratinase ratio of 1.71 for optimal prodigiosin production and WFD. Luedeking–Piret kinetics revealed a non-exclusive, non-growth-associated prodigiosin yield of 9.66 g/L from the degradation of 88.55% waste feather within 96 h. The polyethylene glycol (PEG) 6000/Na+ citrate aqueous two-phase system-purified serratiopeptidase demonstrated gelatinolytic and keratinolytic activities that were stable for 240 h at 55 °C and pH 9.0. In vitro evaluations revealed that the prodigiosin inhibited methicillin-resistant Staphylococcus aureus at IC50 of 4.95 µg/mL, the plant-pathogen, Sclerotinia sclerotiorum, at IC50 of 2.58 µg/mL, breast carcinoma at IC50 of 0.60 µg/mL and 2,2-diphenyl-1-picryl-hydrazyl hydrate (DPPH) free-radical at IC50 of 96.63 µg/mL). The pigment also demonstrated commendable textile dyeing potential of fiber and cotton fabrics. The technology promises cost-effective prodigiosin development through sustainable waste feather-waste frying oil co-management.

Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Cloud workflow scheduling algorithm based on multi-objective hybrid particle swarm optimisation

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

Analysis of Micro Array Gene Expression Data Using Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSO-FWC) Technique

In the rapidly advancing field of genomics, microarray technologies have turned into a ground-breaking system on simultaneous monitoring the expression patterns of multiple genes under various arrangements of constraints. A fundamental errand is to propose diagnostic techniques to distinguish cluster of genes comparative expression designs and are initiated by comparative conditions. And furthermore, the relating investigation has issue is to cluster multi-condition gene expression data. To overcome these issues, the vast measure of data obtained by this technology, resort to clustering methods that distinguish clusters of genes of share similar expression profiles. The motivation of this work is to introduce a clustering method in microarray gene expression data analysis. Multi-Objective Binary Particle Swarm Optimization with Fuzzy Weighted Clustering (MOBPSOFWC) algorithm is proposed to analyze gene expression data. In high dimensionality, a quick heuristic based pre-processing technique is employed to diminish some of the basic domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is implemented in MATLAB tool used for finding further feature subsets. The investigative are directed to distinguish the execution of the proposed work with existing clustering approaches.

A translational motion compensation technique for inverse synthetic aperture radar images using multi‐objective particle swarm optimization algorithm

AbstractIn this study, a novel multi‐objective (MO) motion compensation (MC) technique is proposed to clear blur effects in the inverse synthetic aperture radar (ISAR) images resulted by the translational motion of the target. Thanks to the MO particle swarm optimization (PSO) algorithm, the velocity and acceleration of the target which minimize the entropy and maximize the contrast of the ISAR image are optimally determined. In order to find an optimal solution from trade‐off solutions between entropy and contrast, Pareto front technique is exploited. To demonstrate the performance of the algorithm, the proposed method is implemented for the four ISAR scenarios reported elsewhere, and compared with the single‐objective meta‐heuristic optimization algorithms (artificial bee colony, genetic algorithm, and PSO with island model) implemented in the literature. Furthermore, the accuracy of the proposed technique is numerically pointed out by comparing the entropy and contrast values of each scenarios with the actual values. The results obviously indicate that the proposed MO MC technique is very successful and efficient compared to single‐objective algorithms and performance of the technique is higher than the other methods as the velocity and the acceleration increases.

An Improved Multi-Objective Quantum-Behaved Particle Swarm Optimization for Railway Freight Transportation Routing Design

With the development of railway transportation, the railway transportation enterprises expand their freight transportation from station-to-station transportation to door-to-door transportation, which makes the routing design more complicated. The existing classical optimization algorithms are difficult to meet the needs of practical applications. Therefore, the paper introduces an Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm (IMOQPSO). Then based on the continuous coding for the Railway Freight Transportation Routing Design, the proposed improved algorithm was applied to solve the problem to verify the performance of algorithm. Finally, the paper compared the performance of Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm with other four continuous multi-objective swarm intelligence algorithms. The results shown that the proposed algorithm obtained the best Pareto front which is closer to the real Pareto front of Railway Freight Transportation Routing Design. Hence, the proposed Improved Multi-objective Quantum-behaved Particle Swarm Optimization algorithm can provide support for the railway transport enterprises routing design decisions to some extent.

Decomposition-based multi-objective comprehensive learning particle swarm optimisation

This paper proposes decomposition-based comprehensive learning particle swarm optimisation (DCLPSO) for multi-objective optimisation. DCLPSO uses multiple swarms, with each swarm optimising a separate objective. Two sequential phases are conducted: independent search and then cooperative search. Important information related to extreme points of the Pareto front often can be found in the independent search phase. In the cooperative search phase, a particle randomly learns from its personal best position or an elitist on each dimension. Elitists are non-dominated solutions and are stored in an external repository shared by all the swarms. Mutation is applied to each elitist in this phase to help escaping from local Pareto fronts. Experiments conducted on various benchmark problems demonstrate that DCLPSO is competitive in terms of convergence and diversity of the resulting non-dominated solutions.

Economic Dispatch of the Low-Carbon Green Certificate with Wind Farms Based on Fuzzy Chance Constraints

As the low-carbon economy continues to expand, wind power, as one form of clean energy, promotes the low-carbon power development process. In this paper, a multi-objective environmental economic dispatch (EED) model is proposed considering multiple uncertainties of the system. Carbon trading costs and green certificate trading costs are introduced into the economic costs. Meanwhile, the objective function of pollutant emissions is taken into account in the model, which can further promote the reduction of pollutant emissions in the system scheduling. The output of wind turbines is uncertain and volatile, so it brings new challenges to the power system EED once the large-scale wind power accesses the power grid. For the multiple uncertainties of the system, fuzzy chance-constrained programming is introduced, and the output of the wind turbines and the load are regarded as fuzzy variables. We use the clear equivalence forms to clarify the fuzzy chance constraints. The improved multi-objective standard particle swarm optimization (SPSO) algorithm is used to solve the optimization problem effectively. The feasibility and effectiveness of the proposed model and algorithm are verified by an example of a 10-unit system with two wind farms.

Particle swarm evolutionary computation-based framework for optimizing the risk and cost of low-demand systems of nuclear power plants

ABSTRACT In this paper, an adapted multi-objective multi-swarm co-evolutionary particle swarm optimization (PSO) framework is developed to simultaneously optimize the risk and cost of low-demand systems of nuclear power plants (NPPs). In the built framework, multi-swarm co-evolutionary strategy is introduced to handle the fitness assignment puzzle of multi-objective optimization problems. Besides, to deal with the mixed-integer problem of the decision variables vector, a sub-interval covering-based nearest boundary method is also adopted. To illustrate the effectiveness and efficiencies of the proposed method, a typical high-pressurized injection system (HPIS) is analyzed. The results indicate that, compared with the classic non-dominated sorting genetic algorithm (NSGA)-II approach, the proposed method is more simple and easier to be convergent, besides, of which the Pareto front is better distributed.

A Multiple Core Execution for Multiobjective Binary Particle Swarm Optimization Feature Selection Method with the Kernel P System Framework

Membrane computing is a theoretical model of computation inspired by the structure and functioning of cells. Membrane computing models naturally have parallel structure, and this fact is generally for all variants of membrane computing like kernel P system. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses multiple cores processing tools in MATLAB as a parallel tool to implement proposed feature selection method based on kernel P system-multiobjective binary particle swarm optimization to identify marker genes for cancer classification. Through this implementation, the proposed feature selection model will involve all the features of a P system including communication rule, division rule, parallelism, and nondeterminism.

Task scheduling using multi-objective hamming discrete particle swarm optimisation in distributed systems

Task Scheduling (TS) is one of the crucial issues in distributed systems (DS). Finding an optimal schedule for such an environment is NP-hard. Heuristics/meta-heuristics are the efficient technologies for solving NP-hard problems. The well-known existing meta-heuristics such as differential evolution and genetic algorithm need evolutionary operators for finding the solution. Discrete particle swarm optimisation (DPSO) is a recent meta-heuristic technique, which does not need such type of operators to find the optimal solution. This paper presents a modified DPSO. The proposed modified DPSO uses hamming distance to update the particles in the swarm. This kind of distance-based updating technique ensures that all the particles fly only towards the leader particle. Make span, flow time and reliability cost are performance measures to evaluate the efficiency of the proposed DPSO algorithm. Computational simulations are performed based on a set of benchmark instances to evaluate the performance of the proposed algorithm.

A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address the multiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.

Multiobjective optimization of complex antenna structures using response surface models

A very efficient multiobjective (MO) design technique for complex antenna structures involving a large number of design parameters is presented. This design technique, multiobjective-fractional factorial design (MO-FFD), is very different from conventional Pareto-based MO algorithms, which take a great deal of effort to balance the trade-off between all the design specifications. By performing one single combination of simulations, all the response surface models of design goals are simultaneously built, and Derringer's desirability functions are readily applied to these models so that the optimum structure is obtained. Compared to classical MO algorithms such as Strength Pareto Evolutionary Algorithm 2, nondominated sorting particle swarm optimizer, and cultural MO particle swarm optimization, MO-FFD yields more desirable performances yet the required number of simulations is reduced by 97%. This article thoroughly illustrates the mathematical development of MO-FFD, deriving a novel application of ultrawideband (UWB) antennas because of its MO optimization capability. More explicitly, MO-FFD overcomes all the design challenges of dual band-notched UWB antennas including desired impedance characteristics, enhanced fidelity factors, and uniform peak gains over the passband, which are what conventional Pareto-based algorithms cannot attain. The measured results show that all the performance criteria are met; especially, the time-domain signal distortion is minimized. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 26:62–71, 2016.

Cooperative weapon-target assignment based on multi-objective discrete particle swarm optimization-gravitational search algorithm in air combat

Cooperative weapon-target assignment based on multi-objective discrete particle swarm optimization-gravitational search algorithm in air combat

A particle swarm approach for multi-objective optimization of electrical discharge machining process

This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle ...

Applying nonlinear MODM model to supply chain management with quantity discount policy under complex fuzzy environment

Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bifuzzy variables. By taking into account the strategy and the process of decision making, a bifuzzy nonlinear multiple objective decision making (MODM) model is presented to solve the proposed problem. Design/methodology/approach: The bi-fuzzy variables in the MODM model are transformed into the trapezoidal fuzzy variables by the DMs's degree of optimism α 1 and α 2 , which are de-fuzzified by the expected value index subsequently. For solving the complex nonlinear model, a multi-objective adaptive particle swarm optimization algorithm (MO-APSO) is designed as the solution method. Findings: The proposed model and algorithm are applied to a typical example of SCM problem to illustrate the effectiveness. Based on the sensitivity analysis of the results, the bifuzzy nonlinear MODM SCM model is proved to be sensitive to the possibility level α 1 .

Modified Multiobjective Dynamic Multi-Swarm Particle Swarm Optimization for Mineral Grinding Process

The mineral grinding process is a typical constrained multi-objective optimization problem for its two main goals are quality and quantity. This paper established a similarity criterion mathematical model and combined Multi-objective Dynamic Multi-Swarm Particle Swarm Optimization with modified feasibility rule to optimize the two goals. The simulation results showed that the results of high quality were achieved and the Pareto frontier was evenly distributed and the proposed approach is efficient to solve the multi-objective problem for the mineral grinding process.

Fixed structure constrained preview control design using enhanced PSO approach

This paper proposes the design of fixed structure preview controller with multiple objectives in constrained environment using hybrid technique based on co-evolutionary particle swarm optimisation and marriage in honey bees optimisation algorithm. The paper, also, presents a comparative evaluation of the commonly used constraint - handling approaches in evolutionary algorithms with the proposed hybrid multi-objective constrained co-evolutionary particle swarm optimisation MOCC-PSO procedure. The available procedures and the proposed algorithm are evaluated and verified using MATLAB platform for engineering design problems, namely autonomous control of under-water vehicle and 2-DOF helicopter. The results validate the ability of the algorithm in terms of the quality of solution obtained in the constrained environment and the ease to implement the non-classical objectives and constraints.

The Planning of Distribution Generation (DG) Based on Multi-Objective Quantum Particle Swarms Optimization (QPSO)

According to economic-technical optimization objective of distribution network to which distributed generation is added, a multi-objective model is proposed in this paper. The model contains DG construction investment and operation fee, network loss, reliability, as well as environmental factor. and then puts forward the multi-objective quantum particle swarm optimization (QSPO) algorithm, and the distributed power supply after installation position and capacity for the comprehensive planning research. The result proves that QPSO has advantages of speedy searching for the optimum and keeping the population diversity. Compared to Particle Swarms Optimization (PSO), QSPO shows high efficiency and robustness.

Multiobjective Optimization of a Benfield HiPure Gas Sweetening Unit

We show how a multiobjective bare-bones particle swarm optimization can be used for a process parameter tuning and performance enhancement of a natural gas sweetening unit. This has been made through maximization of hydrocarbon recovery and minimization of the total energy of the process as the two objectives of the optimization. A trade-off exists between these two objectives as illustrated by the Pareto front. This algorithm has been applied to a sweetening unit that uses the Benfield HiPure process. Detailed models of the natural gas unit are developed in ProMax process simulator and integrated to the multi-objective optimization developed in visual basic environment (VBA). In this study, the solvent circulation rates, stripper pressure and reboiler duties are considered as the decision variables while hydrogen sulfide and carbon dioxide concentrations in the sweetened gas are considered as process constraints. The upper and lower bounds of the decision variables are obtained through a parametric sensitivity analysis of the models. The Pareto sets show a significant improvement in hydrocarbon recovery and a decent reduction in the heat consumption of the process.