Objective Particle Swarm Optimization Research Articles

A novel hybrid GA–PSO framework for mining quantitative association rules

Discovering association rules is a useful and common technique for data mining in which dependencies among datasets are shown. Discovering the rules from continuous numeric datasets is one of the common challenges in data mining. Furthermore, another restriction imposed by algorithms in this area is the need to determine the minimum threshold for the criteria of support and confidence. By drawing on two heuristic optimization techniques, to wit, the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, a hybrid algorithm for extracting quantitative association rules was developed in this research. Accurate and interpretable rules result from the integration of the multiple objectives GA with the multiple objective PSO algorithms, which redresses the balance in the exploitation and exploration tasks. The useful and appropriate rules and the most suitable numerical intervals are discovered by proposing a multi-criteria method in which there is no need to discretize numerical values and to determine threshold values of minimum support and confidence. Different criteria are used to determine appropriate rules. In this algorithm, the selected rules are extracted based on confidence, interestingness and comprehensibility. The results gained over five real-world datasets evidence the effectiveness of the proposed method. By hybridization of the GA and the PSO algorithm, the proposed approach has achieved considerable improvements compared with the basic algorithms in the criteria of the number of extracted rules from dataset, high confidence measure and support percentage.

A Multi-Objective Particle Swarm Optimization for Pavement Maintenance with Chaos and Discrete

Particle Swarm Optimization (PSO) is a very common algorithm in swarm intelligence algorithms. PSO has been used to solve a lot of problems with one or more goals. Actuality, the multi-objectives improvement issues in all real life are combinatorial in nature. Therefore, PSO has been improved to be able to handle very large number of decision variables and reduce or decrease computational complexity. In this work, a chaos multi objective PSO algorithm is improved for solving discrete (binary) optimization issues with crossover operation. The developed Chaos Discrete Multi Objective PSO (CDMOPSO) algorithm is applied to pavement management problem for flexible pavement to get optimal maintenance and rehabilitation plan. The results shown that there is significant improvement in the solutions satisfying pavement conditions and maintenance cost goals. It is required to a very short time of execution by the improved algorithm to reach a very good solution. Also, comparing the convergence of solutions with the rest of the PSO algorithms, it has found that the suggested algorithm is better.

A mathematical model for designing an integrated pharmaceutical supply chain with maximum expected coverage under uncertainty

This study presents an extension of the integrated Pharmaceutical Supply Chain design with maximum expected coverage, where different hospitals with specific reliability value for different pharmaceutical substances are considered. A four-layer multi-period supply chain including manufacturers, Distribution Centers, hospitals and patients is considered in this research. To increase the supply chain reliability, the pharmaceutical substance flow in each individual layer is allowed. Pharmaceutical substance’s priority for each hospital varies according to its patients types. A reliability index for pharmaceutical substances is proposed to consider substances priority and improve the service level of hospitals for patients. A multi-objective model that increases demands’ coverage and minimizes the total costs is developed. Centroid method is applied to handle demand uncertainty and a Multi-objective Non-dominated Ranked Genetic Algorithm is proposed to solve the model. This algorithm can generate a Pareto front with higher quality and better diversity when compared with Multiple Objective Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm II. As a case study, the proposed model is applied for the medical sector in the Fars province of Iran. Results show that when different reliability level for various pharmaceutical substances in different hospitals is considered, significantly better results in terms of unsatisfied demand without a significant increase in costs are obtained. In addition, the proposed approach is able to provide solutions to improve the supply chain reliability in a practical pharmaceutical supply network.

A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem

The 3D indoor deployment of sensor nodes is a complex real world problem, proven to be NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid approach relying on a novel bird’s accent-based many objective particle swarm optimization algorithm (named acMaPSO) to resolve the problem of 3D indoor deployment on the Internet of Things collection networks. The new concept of bird’s accent is presented to assess the search ability of particles in their local areas. To conserve the diversity of the population during searching, particles are separated into different accent groups by their regional habitation and are classified into different categories of birds/particles in each cluster according to their common manner of singing. A particle in an accent-group can select other particles as its neighbors from its group or from other groups (which sing differently) if the selected particles have the same expertise in singing or are less experienced compared to this particle. To allow the search escaping from local optima, the most expert particles (parents) “die” and are regularly replaced by a novice (newborn) randomly generated ones. Moreover, the hybridization of the proposed acMaPSO algorithm with multi-agent systems is suggested. The new variant (named acMaMaPSO) takes advantage of the distribution and interactivity of particle agents. Experimental, numerical and statistical found results show the effectiveness of the two proposed variants compared to different other recent state-of-the-art of many-objective evolutionary algorithms.

The density-based clustering method for privacy-preserving data mining.

Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.

A comprehensive modelling, analysis and optimisation of adhesive bonded single lap glass fibre reinforced composite joints

This paper presents a study on joint strength of adhesively bonded single lap glass fibre reinforced composite (GFRC). Taguchi L27 orthogonal array with four factors (adhesive thickness, overlap length, surface roughness, and adherend thickness) operating at three levels was used for conducting experiments and recorded the failure load and shear strength data. The results reveal that, adhesive thickness being the most dominant and surface roughness has less dominant on failure load and shear strength of composite joints. Multi objective particle swarm optimisation based on crowding distance (MOPSO-CD) was used to optimise the bi-objective functions simultaneously under the input variable constraints. The fitness value obtained for bi-objective functions were found to be close to unity (0.944). The confirmation tests were conducted for optimised conditions to know the appropriateness of MOPSO-CD tool. It has been found that at the optimised conditions the improvement is 52.94% and 61.12% respectively, for failure load and shear strength.

Productivity Improvement of a Eco Friendly Warehouse using Multi Objective Optimal Robot Trajectory Planning

Production of environment is one of the top rated objectives of all countries. 10% emission of CO2 in the world is from logistics industries due to freight transports and warehousing operations. Green logistics is an important step to minimise ecological impacts of the logistics operations. Green environment in a warehouse plays a mandatory role in green logistics. Automation, robotics and smart systems give a good contribution in making the warehouse environment clean and green. A method for productivity improvement of a green warehouse using multi objective optimal trajectory planning of a warehouse robot is proposed in this paper. Mixed load palletising operation (build-to-order palletising) is considered. Two multi objective optimisation algorithms such as multi objective particle swarm optimisation (MOPSO) and multi objective differential evolution (MODE) are used. A numerical example on an Industrial robot (MTAB ARISTO 6XT robot) is presented. An economic and productivity analysis is carried out. The obtained results proved that the multi objective optimisation on warehouse robot trajectory planning enhances supply chain productivity and profits.

A comprehensive modelling, analysis and optimisation of adhesive bonded single lap glass fibre reinforced composite joints

This paper presents a study on joint strength of adhesively bonded single lap glass fibre reinforced composite (GFRC). Taguchi L27 orthogonal array with four factors (adhesive thickness, overlap length, surface roughness, and adherend thickness) operating at three levels was used for conducting experiments and recorded the failure load and shear strength data. The results reveal that, adhesive thickness being the most dominant and surface roughness has less dominant on failure load and shear strength of composite joints. Multi objective particle swarm optimisation based on crowding distance (MOPSO-CD) was used to optimise the bi-objective functions simultaneously under the input variable constraints. The fitness value obtained for bi-objective functions were found to be close to unity (0.944). The confirmation tests were conducted for optimised conditions to know the appropriateness of MOPSO-CD tool. It has been found that at the optimised conditions the improvement is 52.94% and 61.12% respectively, for failure load and shear strength.

Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution

The time, cost and quality are crucial, conflicting aspects to construction project management. Tradeoff optimization among project duration (time), project cost, and the project quality within the project scope is necessary to enhance overall construction project benefit. A novel optimization algorithm, Opposition-based Multiple Objective Differential Evolution (OMODE), is presented to solve the time-cost-quality tradeoff (TCQT) problem. This novel algorithm employs an opposition-based learning technique for population initialization and for generation jumping. Opposition numbers are used to improve the exploration and convergence performance of the optimization process. A numerical high-way construction project case study is analyzed to illustrate the use of the algorithm and demonstrate its capabilities in generating non-dominated solutions. Comparisons with non-dominated sorting genetic algorithm (NSGA-II), multiple objective particle swarm optimization (MOPSO), multiple objective differential evolution (MODE) and previous results verify the efficiency and effectiveness of the proposed algorithm. This study is expected to provide an alternative solving methodology for the TCQT problem and help project manager plan construction methods with optimal time-cost-quality tradeoff.

Selected issues of multi-objective WLAN planning

The aim of the article is to apply a multicriteria approach to MOO (Multi Objective Optimization) planning for WLAN (Wireless Local Area Network) using selected swarm optimization methods. For this purpose, in the process of searching for the extremum of two criterion functions, which are an optimization index, two swarm algorithms were used: MOCS (Multi Objective Cuckoo Search) and MOPSO (Multi Objective Particle Swarm Optimization). The results were compared with the single-criterion SOO (Single Objective Optimization) range-based network planning based on the regular distribution of TP (test point) using the CS Cuckoo Search algorithm.

Optimized Rectangular Electrode of Electrostatic Sensor

This paper focuses on optimization of rectangular electrodes of electrostatic sensors. Fundamental characteristics of these electrodes are significant in order to use them in many industries that deal with particles and powders. In this study, Multi- Objective Particle Swarm Optimization (MOPSO) technique has been employed to obtain optimal value of electrode designs including length and thickness. Commercial MATLAB software is used in the optimization. Special sensitivity and statistical error have been utilized as conflict objective functions to recognize optimal sizes of electrodes. The optimization results are compared with the experimental data available in current study. From the observed results, the optimum sizes of length and thickness of rectangular electrode was equal to 0.6 cm and 0.5 cm, respectively.

Design and analysis of novel optimal harmonic elimination strategy based on MOPSO to obliterate low‐order harmonics of odd‐nary cascaded transformers‐based multilevel inverter

Creating a high‐step staircase sinusoidal voltage taking into account of low number of switches is recognized as a significant challenge ahead of inventors of power electronics. This paper suggests a maiden cascaded transformer‐based multilevel inverter to effectively develop and solve the abovementioned challenge. Hence, via appending the bidirectional switches to the capacitors of submultilevel inverter as well as low‐frequency transformers, the proposed inverter structure can flexibly operate as so‐called “odd‐nary asymmetrical multilevel inverter.” From the perspective of low‐order harmonics produced by all inverter structures, apart from inverter type, it is essential that an effective strategy to be implemented to eliminate these harmonics. Toward this subject, kerf‐style Selective Harmonic Elimination Pulse Width Modulation (SHEPWM) has been scheduled as an optimization problem. Because of nature of multiobjective state of the problem, application of the multiobjective optimization technique is inevitable. Considering significant performance of Multi Objective Particle Swarm Optimization (MOPSO) for solving the nonlinear objectives, the elimination of fifth, seventh, and 11th harmonics is formulated by relevant fitness functions based on MOPSO. Putting it all together, the relevant analytical study accompanying both the simulation and experimental results has transparently verified the performance of the proposed multilevel inverter. Furthermore, elimination of all three designated low‐order harmonics, ie, fifth, seventh, and 11th harmonics using MOPSO‐based kerf‐style SHEPWM strategy has been well confirmed.

MLBC: Multi-objective Load Balancing Clustering technique in Wireless Sensor Networks

In Wireless Sensor Networks (WSN) there are scenarios with conflicting objectives. This needs to be modeled as multi-objective optimization formulation. The optimization problem changes with the nature of application, network scenario and input/output parameters. Depending upon the underlying application requirements, there is a need of an appropriate Multi-Objective Optimization technique to manage these multiple conflicting objectives simultaneously and to yield an overall optimal solution. In this paper, a novel Multi-objective Load Balancing Clustering (MLBC) technique is proposed by utilizing Multi Objective Particle Swarm Optimization (MOPSO). Two objective functions are defined: Energy Efficiency and Reliability. The energy efficiency is based on the average residual energy of Cluster Heads (CHs), whereas the reliability is based on the communication cost of inter-cluster routing. The nodes transmit their information to Cluster Head or Base Station in single-hop or multi-hop manner. A healing function is utilized to avoid loops in the generated path. The load balancing is performed by shuffling the roles of next hop node and Cluster Head in each iteration. The best compromise solution is selected through the fuzzy decision-based approach. The performance of the proposed and existing approaches has been evaluated in terms of energy efficiency, network lifetime, packet delivery ratio, data accuracy and number of active nodes. Apart from these parameters, coverage and scalability are also considered to evaluate the quality of solutions provided by multi-objective optimization approach.

An Improved Multi-Objective Particle Swarm Optimization Based on Utopia Point Guided Search

This article demonstrates the implementation of a novel local search approach based on Utopia point guided search, thus improving the exploration ability of multi- objective Particle Swarm Optimization. This strategy searches for best particles based on the criteria of seeking solutions closer to the Utopia point, thus improving the convergence to the Pareto-optimal front. The elite non-dominated selected particles are stored in an archive and updated at every iteration based on least crowding distance criteria. The leader is chosen among the candidates in the archive using the same guided search. From the simulation results based on many benchmark tests, the new algorithm gives better convergence and diversity when compared to existing several algorithms such as NSGA-II, CMOPSO, SMPSO, PSNS, DE+MOPSO and AMALGAM. Finally, the proposed algorithm is used to solve mechanical design based multi-objective optimization problems from the literature, where it shows the same advantages.

Multi - focus image fusion using self - resemblance measure

Image fusion is the process of integrating several source images into a single image that provides more reliable information along with reduced redundancy. Inspired by the pixel correlation property, a fusion rule based on self-resemblance measure (SRM) is proposed. An efficient image fusion algorithm is presented for fusing multi-focus images using self resemblance measure, consistency verification and multiple objective particle swarm optimization. Initially based on SRM fusion rule, the source images are fused and then the block sizes are adaptively selected by particle swarm optimization based on the multiple objective fitness functions for obtaining enhanced fused image. The proposed framework is evaluated using quantitative metrics such as root mean square error, peak signal to noise ratio, correlation, standard deviation, mutual information and Petrovic metric. Experimental results demonstrate the outperformance of the proposed algorithm over many other well known state-of-the-art fusion techniques, both in visual effect and objective evaluation criteria.

Enhanced continuous and discrete multi objective particle swarm optimization for text summarization

Reviews from various domains is being posted in web increasingly day by day. Analyzing this enormous content would be useful in decision making for various stakeholders. Text summarization techniques generate concise summaries including sentiments which are useful in analyzing the large content. So text summarization systems become significant in analyzing this huge content. The summaries are generated based on important features using multi objective approaches where sufficient literature is not available. Major limitations of text summarization systems are scalability and performance. Two variants of multi objective optimization techniques such as Discrete and Continuous which work under the principles of particle swarm optimization (PSO) for extractive summarization of reviews had been proposed for performance improvement. The performance is validated using Recall-Oriented Understanding for Gisting Evaluation (ROUGE), Success Counting (SC) and Inverted Generational Distance (IGD). Based on the experimental results it is found that the system is effective using multi-objective PSO algorithm when compared to other state-of-art approaches like Liu’s approach feature based binary particle swarm optimization and etc. for feature based review summarization.

Particle swarm optimization procedure in determining parameters in Chaboche kinematic hardening model to assess ratcheting under uniaxial and biaxial loading cycles

AbstractAs parameters in Chaboche model are difficult to be determined from experimental data, a single objective particle swarm optimization procedure was employed to obtain them. Hysteresis loop and uniaxial and biaxial ratcheting simulations were conducted to validate the determined models. Chaboche models determined by particle swarm optimization give more accurate simulation of ratcheting compared with the model determined by trial and error method. Chaboche models containing different backstress components were studied. Models determined considering uniaxial ratcheting can only predict uniaxial ratcheting precisely, while giving very bad simulation of biaxial ratcheting. The linear hardening rule in the N3L1 model clearly decreases the rate of the accumulation of ratcheting strain, and the N3L1 model gives the best simulations. For biaxial ratcheting, the fourth backstress component can decrease the rate of the accumulation apparently, while it has a little influence on prediction of uniaxial ratcheting.

Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem

Particle swarm optimization (PSO) is one of the most popular and successful optimization algorithms used for solving single objective and multi-objective optimization problems. It is found that the Multi objective particle swarm optimization (MOPSO) has ability to find the optimal solution quickly and more efficient than other optimization algorithms. In this paper, a discrete (binary) MOPSO with chaos methods is developed and applied to pavement maintenance management. The main objective of this research is to find optimal maintenance and rehabilitation plan for flexible pavement with minimum maintenance cost and maximum pavement performance. This research is the first attempt to combine the crossover operation with velocity and position with multi objective PSO algorithm. The results show that the improvements in pavement performance and cost objectives are 94.65 and 54.01% respectively, while the improvement in execution time is 99.9%. In addition, it is found that the developed algorithm is able to converge to the optimal solution quickly, comparing with another PSO algorithm.

Robust Optimization for Integrated Construction Scheduling and Multiscale Resource Allocation

This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi‐time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data‐Driven Construction Scheduling and Multiscale Resource Allocation Problem (DD‐CS&MRAP). Based on likelihood robust optimization, a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double‐layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization (MOPSO) and interior point method named MOPSO‐interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO‐interior point algorithm can reduce resource cost and improve the efficiency of resource utilization.

Stochastic multi‐objective framework for optimal dynamic planning of interconnected microgrids

This paper proposes a new stochastic multi-objective framework for optimal dynamic planning of interconnected microgrids (MGs) under uncertainty from economic, technical, reliability and environmental viewpoints. In the proposed approach, optimal site, size, type, and time of distributed energy resources are determined along with optimal allocation of section switches to partitioning conventional distribution system into a number of interconnected MGs. The uncertainties of the problem are considered using scenario modelling and backward scenario reduction technique is implemented to deal with computational burden. In addition, three different risk averse, risk neutral and risk seeker strategies are defined for distribution network operator. The proposed framework is considered as two unparalleled objective functions which the first objective minimizes the investment cost, operation and maintenance cost, power loss cost and pollutants emission cost and the second objective is defined to minimize energy not supplied in both connected and islanded modes of MGs. Finally, multi objective particle swarm optimization is applied to minimize the proposed bi-objective functions and subsequently fuzzy satisfying method is accomplished to select the best solution proportional to risk based strategies. Efficiency of the proposed framework is validated on 85-bus distribution system and obtained results are presented and discussed.