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Article
Publication date: 4 January 2016

Gonggui Chen, Lilan Liu, Yanyan Guo and Shanwai Huang

For one thing, despite the fact that it is popular to research the minimization of the power losses in power systems, the optimization of single objective seems insufficient to…

Abstract

Purpose

For one thing, despite the fact that it is popular to research the minimization of the power losses in power systems, the optimization of single objective seems insufficient to fully improve the performance of power systems. Multi-objective VAR Dispatch (MVARD) generally minimizes two objectives simultaneously: power losses and voltage deviation. The purpose of this paper is to propose Multi-Objective Enhanced PSO (MOEPSO) algorithm that achieves a good performance when applied to solve MVARD problem. Thus, the new algorithm is worthwhile to be known by the public.

Design/methodology/approach

Motivated by differential evolution algorithm, cross-over operator is introduced to increase particle diversity and reinforce global searching capacity in conventional PSO. In addition to that, a constraint-handling approach considering Constrain-prior Pareto-Dominance (CPD) is presented to handle the inequality constraints on dependent variables. Constrain-prior Nondominated Sorting (CNS) and crowding distance methods are considered to maintain well-distributed Pareto optimal solutions. The method combining CPD approach, CNS technique, and cross-over operator is called the MOEPSO method.

Findings

The IEEE 30 node and IEEE 57 node on power systems have been used to examine and test the presented method. The simulation results show the MOEPSO method can achieve lower power losses, smaller voltage deviation, and better-distributed Pareto optimal solutions comparing with the Multi-Objective PSO approach.

Originality/value

The most original parts include: the presented MOEPSO algorithm, the CPD approach that is used to handle constraints on dependent variables, and the CNS method which is considered to maintain a well-distributed Pareto optimal solutions. The performance of the proposed algorithm successfully reflects the value of this paper.

Details

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering, vol. 35 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 27 November 2018

Souhil Mouassa and Tarek Bouktir

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single…

Abstract

Purpose

In the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.

Design/methodology/approach

A MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.

Findings

Three test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.

Originality/value

The proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 38 no. 1
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 17 January 2020

Yi Zhang, Haihua Zhu and Dunbing Tang

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the…

Abstract

Purpose

With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed.

Design/methodology/approach

After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization.

Findings

Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence.

Research limitations/implications

This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown.

Practical implications

IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum.

Social implications

This research provides an efficient scheduling method for solving the FJSP problem.

Originality/value

This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

Details

Kybernetes, vol. 49 no. 12
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 1 April 2019

Arulraj Rajendran and Kumarappan Narayanan

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as…

Abstract

Purpose

This paper aims to optimally plan distributed generation (DG) and capacitor in distribution network by optimizing multiple conflicting operational objectives simultaneously so as to achieve enhanced operation of distribution system. The multi-objective optimization problem comprises three important objective functions such as minimization of total active power loss (Plosstotal), reduction of voltage deviation and balancing of current through feeder sections.

Design/methodology/approach

In this study, a hybrid configuration of weight improved particle swarm optimization (WIPSO) and gravitational search algorithm (GSA) called hybrid WIPSO-GSA algorithm is proposed in multi-objective problem domain. To solve multi-objective optimization problem, the proposed hybrid WIPSO-GSA algorithm is integrated with two components. The first component is fixed-sized archive that is responsible for storing a set of non-dominated pareto optimal solutions and the second component is a leader selection strategy that helps to update and identify the best compromised solution from the archive.

Findings

The proposed methodology is tested on standard 33-bus and Indian 85-bus distribution systems. The results attained using proposed multi-objective hybrid WIPSO-GSA algorithm provides potential technical and economic benefits and its best compromised solution outperforms other commonly used multi-objective techniques, thereby making it highly suitable for solving multi-objective problems.

Originality/value

A novel multi-objective hybrid WIPSO-GSA algorithm is proposed for optimal DG and capacitor planning in radial distribution network. The results demonstrate the usefulness of the proposed technique in improved distribution system planning and operation and also in achieving better optimized results than other existing multi-objective optimization techniques.

Details

International Journal of Energy Sector Management, vol. 13 no. 1
Type: Research Article
ISSN: 1750-6220

Keywords

Article
Publication date: 20 July 2021

Prashant R. Dike, T.S. Vishwanath and Vandana Rohakale

Since communication usually accounts as the foremost problem for power consumption, there are some approaches, such as topology control and network coding (NC), for diminishing…

Abstract

Purpose

Since communication usually accounts as the foremost problem for power consumption, there are some approaches, such as topology control and network coding (NC), for diminishing the activity of sensors’ transceivers. If such approaches are employed simultaneously, then the overall performance does raise as expected. In a wireless sensor network (WSN), the linear NC has been shown to enhance the performance of network throughput and reduce delay. However, the NC condition of existing NC-aware routings may experience the issue of false-coding effect in some scenarios and usually neglect node energy, which highly affects the energy efficiency performance. The purpose of this paper is to propose a new NC scheduling in a WSN with the intention of maximizing the throughput and minimizing the energy consumption of the network.

Design/methodology/approach

The improved meta-heuristic algorithm called the improved mutation-based lion algorithm (IM-LA) is used to solve the problem of NC scheduling in a WSN. The main intention of implementing improved optimization is to maximize the throughput and minimize the energy consumption of the network during the transmission from the source to the destination node. The parameters like topology and time slots are taken for optimizing in order to obtain the concerned objective function. While solving the current optimization problem, it has considered a few constraints like timeshare constraint, data-flow constraint and domain constraint. Thus, the network performance is proved to be enhanced by the proposed model when compared to the conventional model.

Findings

When 20 nodes are fixed for the convergence analysis, performed in terms of multi-objective function, it is noted that during the 400th iteration, the proposed IM-LA was 10.34, 13.91 and 50% better than gray wolf algorithm (GWO), firefly algorithm (FF) and particle swarm optimization (PSO), respectively, and same as LA. Therefore, it is concluded that the proposed IM-LA performs extremely better than other conventional methods in minimizing the cost function, and hence, the optimal scheduling of nodes in a WSN in terms of the multi-objective function, i.e. minimizing energy consumption and maximizing throughput using NC has been successfully done.

Originality/value

This paper adopts the latest optimization algorithm called IM-LA, which is used to solve the problem of network coding scheduling in a WSN. This is the first work that utilizes IM-LA for optimal network coding in a WSN.

Details

International Journal of Intelligent Unmanned Systems, vol. 10 no. 4
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 28 January 2020

Datta Bharadwaz Y., Govinda Rao Budda and Bala Krishna Reddy T.

This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of particle…

90

Abstract

Purpose

This paper aims to deal with the optimization of engine operational parameters such as load, compression ratio and blend percentage of fuel using a combined approach of particle swarm optimization (PSO) with Derringer’s desirability.

Design/methodology/approach

The performance parameters such as brake thermal efficiency (BTHE), brake specific fuel consumption (BSFC), CO, HC, NOx and smoke are considered as objectives with compression ratio, blend percentage and load as input factors. Optimization is carried out by using PSO coupled with the desirability approach.

Findings

From results, the optimum operating conditions are found to be at compression ratio of 18.5 per cent of fuel blend and 11 kg of load. At this input’s parameters of the engine, outputs performance parameters are found to be 34.84 per cent of BTHE, 0.29 kg/kWh of BSFC, 2.86 per cent of CO, 13 ppm of HC, 490 ppm of NOx and 26.25 per cent of smoke.

Originality/value

The present study explores the abilities of both particle swarm algorithm and desirability approach when used together. The combined approach resulted in faster convergence and better prediction capability. The present approach predicted performance characteristics of the variable compression ratio engine with less than 10 per cent error.

Details

World Journal of Engineering, vol. 17 no. 2
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 3 September 2019

Hongyao Shen, Xiaoxiang Ye, Guanhua Xu, Linchu Zhang, Jun Qian and Jianzhong Fu

During the 3D printing process, the model needs to add a support structure to ensure structural stability. Excessive support structure reduces printing efficiency and results in…

Abstract

Purpose

During the 3D printing process, the model needs to add a support structure to ensure structural stability. Excessive support structure reduces printing efficiency and results in material cost. A flexible support platform for 3 D printing has been designed. It can form an external support structure to replace the original support structure. This paper aims to study the influence of a model’s build orientation on properties when the model is printed on the platform, aiming to provide users with suitable solutions.

Design/methodology/approach

A fitness function for estimating the support structure with a support length is constructed. The particle swarm optimization (PSO) algorithm is modified and applied to find the build orientation that minimizes the support structure. However, when the model is printed on the platform, the build orientation of the minimum support structure enhances the complexity of the working path, resulting in an increase of printing time, which needs to be avoided. This paper applies a multi-objective particle swarm optimization (MOPSO) algorithm to minimize the support structure while minimizing printing time. The Pareto solution is obtained by the algorithm.

Findings

It is found that the model that has the cantilever structure can reduce more support structure after optimization on the platform, when there is surface quality requirement. When there is no limit, the modified algorithm can minimize the support structure of each model. Considering support structure and printing time, the MOPSO algorithm can easily get optimization results to guide the practical work.

Originality/value

This paper optimizes the model’s build orientation on the flexible support platform by PSO, thereby reducing material cost and improving work efficiency.

Book part
Publication date: 5 October 2018

Nima Gerami Seresht, Rodolfo Lourenzutti, Ahmad Salah and Aminah Robinson Fayek

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and…

Abstract

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

Details

Fuzzy Hybrid Computing in Construction Engineering and Management
Type: Book
ISBN: 978-1-78743-868-2

Keywords

Article
Publication date: 30 April 2020

Mehdi Darbandi, Amir Reza Ramtin and Omid Khold Sharafi

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure…

Abstract

Purpose

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.

Design/methodology/approach

In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.

Findings

The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.

Originality/value

As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

Details

International Journal of Pervasive Computing and Communications, vol. 16 no. 2
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 26 October 2012

B. Latha Shankar, S. Basavarajappa and Rajeshwar S. Kadadevaramath

The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with…

Abstract

Purpose

The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.

Design/methodology/approach

To optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.

Findings

This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.

Originality/value

Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

Details

Journal of Modelling in Management, vol. 7 no. 3
Type: Research Article
ISSN: 1746-5664

Keywords

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