TOVEC: Task Optimization Mechanism for Vehicular Clouds using Meta-heuristic Technique

AbstractThis study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.

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Application of failure mode and effects analysis to reduce microplastic emissions

Waste Management & Research ◽

10.1177/0734242x211003133 ◽

2021 ◽

pp. 0734242X2110031

Author(s):

Ana Pires ◽

Paula Sobral

Keyword(s):

Failure Mode ◽

Quantitative Methods ◽

Policy Instruments ◽

Effect Analysis ◽

Advantages And Disadvantages ◽

Heuristic Technique ◽

Single Use ◽

Regulatory Measures ◽

Plastic Products

A complete understanding of the occurrence of microplastics and the methods to eliminate their sources is an urgent necessity to minimize the pollution caused by microplastics. The use of plastics in any form releases microplastics to the environment. Existing policy instruments are insufficient to address microplastics pollution and regulatory measures have focussed only on the microbeads and single-use plastics. Fees on the use of plastic products may possibly reduce their usage, but effective management of plastic products at their end-of-life is lacking. Therefore, in this study, the microplastic–failure mode and effect analysis (MP–FMEA) methodology, which is a semi-qualitative approach capable of identifying the causes and proposing solutions for the issue of microplastics pollution, has been proposed. The innovative feature of MP–FMEA is that it has a pre-defined failure mode, that is, the release of microplastics to air, water and soil (depending on the process) or the occurrence of microplastics in the final product. Moreover, a theoretical recycling plant case study was used to demonstrate the advantages and disadvantages of this method. The results revealed that MP–FMEA is an easy and heuristic technique to understand the failure-effect-causes and solutions for reduction of microplastics and can be applied by researchers working in different domains apart from those relating to microplastics. Future studies can include the evaluation of the use of MP–FMEA methodology along with quantitative methods for effective reduction in the release of microplastics.

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Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems

Applied Sciences ◽

10.3390/app10041257 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1257 ◽

Cited By ~ 2

Author(s):

Liang Zhang ◽

Quanshen Wei ◽

Lei Zhang ◽

Baojiao Wang ◽

Wen-Hsien Ho

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Social Relationships ◽

Two Stage ◽

Proposed Model ◽

The Social ◽

Item List ◽

Ranking Strategy ◽

High Prediction ◽

Optimization Mechanism

Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.

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Implementation of Heuristic Technique and Genetic Algorithms in Shikaku Puzzle Problem

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.58-60.1860 ◽

2011 ◽

Vol 58-60 ◽

pp. 1860-1865 ◽

Cited By ~ 1

Author(s):

Samuel Lukas ◽

Arnold Aribowo ◽

Steven Christian Halim

Keyword(s):

Genetic Algorithms ◽

Software Testing ◽

Computer Software ◽

Simple Rule ◽

Rectangular Grid ◽

Heuristic Technique ◽

Logic Puzzle

Shikaku is a logic puzzle published by Nikoli at 2005. Shikaku has a very simple rule. This puzzle is played on a rectangular grid. Some of the squares in the grid are numbered. The main objective is to create partitions inside the grid. Each partition must have exactly one number, and the number represents the area of the partition. Then the partition’s shape must be a rectangular or a square. The aim of this research is discussing how can computer software be able to solve the Shikaku problem by implementing heuristic technique and genetics algorithms. Initially the Shikaku problem is inputted into the system. Firstly, the software will solve the problem by applying heuristics methods with some logic rules. All logic rules are created and implemented into the software so that the software can minimize the partitions possibilities to the problem. If this heuristics method still can not solve the problem then genetic algorithms will be executed to find the solution. This paper elaborates from how the problem be modelled and also be implemented until software testing to ensure that the solver worked as expected. The implementation consists of a virtual puzzle board with three different size, genetic algorithms parameters, and ability to create, save, load, and solve puzzle. Software testing is conducted to find how fast the system can solve the problem.

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