scholarly journals The Optimization of the Location of the Cargo in Three-Dimension Shelf: Employing the FP-Tree and the Artificial Fish Swarm Algorithms

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shuiwang Zhang ◽  
Linping Fu ◽  
Rui Wang ◽  
Rong Chen
Keyword(s):  
Energy Consumption ◽  
Research Area ◽  
Three Dimension ◽  
Shelf Stability ◽  
Artificial Fish Swarm Algorithm ◽  
Location Allocation ◽  
Novel Approach ◽  
Swarm Algorithms ◽  
Significant Research ◽  
Artificial Fish Swarm

The allocation issues of the location of the cargo have affected the operational efficiency of retail e-commerce warehouses tremendously. Adjusting the cargo location with the change of the order and the operation of the warehouse is a significant research area. A novel approach employing the FP-Tree and the Artificial Fish Swarm Algorithms is proposed. Firstly, energy consumption and shelf stability are employed for the location-allocation. Secondly, the association rules among product items are obtained by the FP-Tree Algorithm to mine frequent list of items. Furthermore, the frequency and the weight of product items are taken into account to ensure the local stability of the shelf during data mining. Thirdly, another method of the location-allocation is obtained with the objectives of the energy consumption and the overall shelf stability along with the frequent items stored nearby that is conducted by the Artificial Fish Swarm Algorithm. Finally, the picking order distance is obtained through two methods of the location-allocation above. The performance and efficiency of the novel introduced method have been confirmed by running the experiment. The outcomes of the simulation suggest that the introduced method has a higher performance concerning criterion called the picking order distance.

scholarly journals Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Kuan-Cheng Lin ◽  
Sih-Yang Chen ◽  
Jason C. Hung
Keyword(s):  
Feature Selection ◽  
Parameter Optimization ◽  
Combinatorial Problem ◽  
Combinatorial Problems ◽  
Support Vector ◽  
Local Optimum ◽  
Artificial Fish Swarm Algorithm ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Information And Communication

Rapid advances in information and communication technology have made ubiquitous computing and the Internet of Things popular and practicable. These applications create enormous volumes of data, which are available for analysis and classification as an aid to decision-making. Among the classification methods used to deal with big data, feature selection has proven particularly effective. One common approach involves searching through a subset of the features that are the most relevant to the topic or represent the most accurate description of the dataset. Unfortunately, searching through this kind of subset is a combinatorial problem that can be very time consuming. Meaheuristic algorithms are commonly used to facilitate the selection of features. The artificial fish swarm algorithm (AFSA) employs the intelligence underlying fish swarming behavior as a means to overcome optimization of combinatorial problems. AFSA has proven highly successful in a diversity of applications; however, there remain shortcomings, such as the likelihood of falling into a local optimum and a lack of multiplicity. This study proposes a modified AFSA (MAFSA) to improve feature selection and parameter optimization for support vector machine classifiers. Experiment results demonstrate the superiority of MAFSA in classification accuracy using subsets with fewer features for given UCI datasets, compared to the original FASA.

scholarly journals Coalition Formation among the Cooperative Agents for Efficient Energy Consumption

2021 ◽  
Vol 13 (15) ◽  
pp. 8662
Author(s):  
Areej A. Malibari ◽  
Daniyal Alghazzawi ◽  
Maha M. A. Lashin
Keyword(s):  
Saudi Arabia ◽  
Energy Consumption ◽  
Energy Distribution ◽  
Coalition Formation ◽  
Research Area ◽  
Efficient Energy ◽  
Cooperative Agents ◽  
Significant Research ◽  
Distribution Process ◽  
Novel Algorithm

Energy saving is a significant research area in Saudi Arabia; however, significant problems have emerged related to its distribution and consumption. Use of an agent is assumed to combat these problems by forming efficient coalitions to control the energy consumption and energy distribution process. This study presents a novel algorithm for distributing the value calculation among the cooperative agents. This is likely to reduce the consumption of energy and extend the coalition lifetime used. The developed algorithm is compared with the basic modified coalition formation algorithm for evaluating its effectiveness. The results showed a reduction in cooling consumption by 20% after applying optimization algorithms. The amount of reduction in the cooling consumption reflects a 31% reduction in expected cooling costs, without affecting the household comfort. Therefore, the study concludes that DNsys provided better performance than the NNsys.

Approximate Inference Algorithm Based on Adaptive Ant Colony and Artificial Fish Swarm Algorithm for Credal Networks

2011 ◽  
Vol 219-220 ◽  
pp. 1504-1508
Author(s):  
Ying Qu ◽  
Pang Zhou
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Directed Acyclic Graphs ◽  
Ant Colony ◽  
Approximate Inference ◽  
Artificial Fish Swarm Algorithm ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

This paper presents a new algorithm for approximate inference in credal networks (that is, models based on directed acyclic graphs and interval-valued probabilities). Approximate inference in credal networks can be considered as multistage decision in this paper. It is looked as combinatorial optimization problems that obtaining the extreme posteriors from the combinations of various vertices in credal networks. Based on this, the paper combines two intelligence swarm algorithms (ant colony algorithm and artificial fish swarm algorithm) to obtain interval posterior probabilities of query variable for the states of given evidence variables.

Nature inspired algorithms for solving the community detection problem

2017 ◽  
Vol 25 (6) ◽  
pp. 902-914
Author(s):  
Eslam Hussein ◽  
Ahmed Ibrahem Hafez ◽  
Aboul Ella Hassanien ◽  
Aly A Fahmy
Keyword(s):  
Community Detection ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Fitness Function ◽  
Bat Algorithm ◽  
Detection Problem ◽  
Artificial Fish Swarm Algorithm ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Abstract Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the Bat Algorithm (BA) and Artificial Fish Swarm Algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.

A Novel Approach for Optimization in Dynamic Environments Based on Modified Artificial Fish Swarm Algorithm

2016 ◽  
Vol 15 (02) ◽  
pp. 1650010 ◽  
Author(s):  
Danial Yazdani ◽  
Alireza Sepas-Moghaddam ◽  
Atabak Dehban ◽  
Nuno Horta
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Dynamic Environments ◽  
Environment Change ◽  
Problem Space ◽  
Artificial Fish Swarm Algorithm ◽  
Novel Approach ◽  
Moving Peaks Benchmark ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Swarm intelligence algorithms are amongst the most efficient approaches toward solving optimization problems. Up to now, most of swarm intelligence approaches have been proposed for optimization in static environments. However, numerous real-world problems are dynamic which could not be solved using static approaches. In this paper, a novel approach based on artificial fish swarm algorithm (AFSA) has been proposed for optimization in dynamic environments in which changes in the problem space occur in discrete intervals. The proposed algorithm can quickly find the peaks in the problem space and track them after an environment change. In this algorithm, artificial fish swarms are responsible for finding and tracking peaks and several behaviors and mechanisms are employed to cope with the dynamic environment. Extensive experiments show that the proposed algorithm significantly outperforms previous algorithms in most of tested dynamic environments modeled by moving peaks benchmark.

Fuzzy multi-objective location and routing problem

2020 ◽  
Vol 39 (3) ◽  
pp. 3259-3273
Author(s):  
Nasser Shahsavari-Pour ◽  
Najmeh Bahram-Pour ◽  
Mojde Kazemi
Keyword(s):  
Firefly Algorithm ◽  
High Reliability ◽  
Research Area ◽  
Nsga Ii ◽  
Location Routing Problem ◽  
Routing Problem ◽  
Location Allocation ◽  
Multi Objective ◽  
Location Routing ◽  
The Cost

The location-routing problem is a research area that simultaneously solves location-allocation and vehicle routing issues. It is critical to delivering emergency goods to customers with high reliability. In this paper, reliability in location and routing problems was considered as the probability of failure in depots, vehicles, and routs. The problem has two objectives, minimizing the cost and maximizing the reliability, the latter expressed by minimizing the expected cost of failure. First, a mathematical model of the problem was presented and due to its NP-hard nature, it was solved by a meta-heuristic approach using a NSGA-II algorithm and a discrete multi-objective firefly algorithm. The efficiency of these algorithms was studied through a complete set of examples and it was found that the multi-objective discrete firefly algorithm has a better Diversification Metric (DM) index; the Mean Ideal Distance (MID) and Spacing Metric (SM) indexes are only suitable for small to medium problems, losing their effectiveness for big problems.

PERFORMANCE EVALUATION OF CULTURAL ARTIFICIAL BEE COLONY AND CULTURAL ARTIFICIAL FISH SWARM ALGORITHM

2019 ◽  
Vol 6 (4) ◽  
pp. 43
Author(s):  
HADIR ADEBIYI BUSAYO ◽  
TIJANI SALAWUDEEN AHMED ◽  
FOLASHADE O. ADEBIYI RISIKAT ◽  
◽  
◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Artificial Bee Colony ◽  
Artificial Fish Swarm Algorithm ◽  
Bee Colony ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm
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