Determining optimum carob powder adsorbtion for cleaning wastewater: intelligent optimization with electro-search algorithm

2019 ◽  
Vol 26 (8) ◽  
pp. 5665-5679 ◽  
Author(s):  
Bahdisen Gezer ◽  
Utku Kose ◽  
Dmytro Zubov ◽  
Omer Deperlioglu ◽  
Pandian Vasant
Keyword(s):  
Search Algorithm ◽  
Intelligent Optimization ◽  
Carob Powder

Improved Harmony Search Algorithm with Crossover Operation

2013 ◽  
Vol 415 ◽  
pp. 353-356 ◽  
Author(s):  
Hong Gang Xia ◽  
Qing Liang Wang
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Improved Harmony Search ◽  
Improved Harmony Search Algorithm

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant introduce a new crossover operation into HS, and design a strategy to adjust parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks carried out to be tested. The numerical results demonstrated that the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

Normal Parameter Reduction in Soft Set Based on Harmony Search Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 2173-2176
Author(s):  
Zhi Kong ◽  
Guo Dong Zhang ◽  
Li Fu Wang
Keyword(s):  
Data Mining ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Soft Set ◽  
Intelligent Optimization ◽  
Parameter Reduction ◽  
Intelligent Optimization Algorithm ◽  
Normal Parameter

The normal parameter reduction in soft set is difficult to application in data mining because of great calculation quantity. In this paper, the intelligent optimization algorithm, the harmony search algorithm, is applied to solve the problem. The normal parameter reduction model is constructed and the harmony search algorithm is designed. Experience has shown that the method is feasible and fast..

A New Variant Harmony Search Algorithm with Random Mutation Strategy

2014 ◽  
Vol 1065-1069 ◽  
pp. 3425-3428
Author(s):  
Xiu Hong Zhao
Keyword(s):  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Random Mutation ◽  
Local Optima ◽  
Intelligent Optimization ◽  
New Variant ◽  
Mutation Strategy

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method, which has been paid much attention recently. However, intelligent optimization methods are easily trapped into local optima, HS is no exception. In order to improve the performance of HS, a new variant of harmony search algorithm with random mutation strategy (HSRM) is proposed in this paper. The HSRM uses a random mutation strategy to replace the pitch adjusting operation, and dynamically adjust the key parameter pitch adjusting rate (PAR). Experiment results demonstrated that the proposed method is superior to the HS and recently developed variants (IHS, and GHS) and other meta-heuristic algorithm.

A New Variant Harmony Search Algorithm for Unconstrained Numerical Optimization Problems

2013 ◽  
Vol 365-366 ◽  
pp. 174-177
Author(s):  
Hong Gang Xia ◽  
Qing Zhou Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
Intelligent Optimization ◽  
New Variant ◽  
Key Parameter

Harmony search (HS) algorithm is a good meta-heuristic intelligent optimization method and it does depend on imitating the music improvisation process to generate a perfect state of harmony. However, intelligent optimization methods is easily trapped into local optimal, HS is no exception. In order to modify the optimization performance of HS, a new variant of harmony search algorithm is proposed in this paper. The variant integrate the position updating of the particle swarm optimization algorithm with pitch adjustment operation, and dynamically adjust the key parameter pitch adjusting rate (PAR) and bandwidth (BW). Several standard benchmarks are to be tested. The numerical results demonstrated the superiority of the proposed method to the HS and recently developed variants (IHS, and GHS).

Research on Key Techniques of Intelligent Optimization of Batch Production Job Shop Scheduling

2013 ◽  
Vol 753-755 ◽  
pp. 1903-1909
Author(s):  
Yan Hong Zuo ◽  
Ke Ren Zhang
Keyword(s):  
Production Scheduling ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Batch Production ◽  
Nsga Ii ◽  
Intelligent Optimization ◽  
Shop Scheduling ◽  
Weak Part ◽  
Structure Framework

Job-shop scheduling is the very important, but weak part in the integrated manufacturing system. In view of the Job-shop scheduling characteristics for batch production of enterprises, this article analysised batch production scheduling problem in detail; then put out a workshop Intelligent Scheduling Optimization technical framework which contained six-stories. And study the basic theory and key technology of this structure framework in-depth, then proposed object-oriented technology which based on improved tabu search algorithm and NSGA-II algorithm to achieve the intelligent optimization targets , and in the experiment proved it is practical and effective.

A Novel Hybrid Optimization Algorithm of Pattern Search and IWO

2014 ◽  
Vol 687-691 ◽  
pp. 1557-1559
Author(s):  
Hui Hui Xiao
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Pattern Search ◽  
Hybrid Optimization ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Intelligent Optimization ◽  
Hybrid Optimization Algorithm ◽  
Simulation Results ◽  
Swarm Intelligence Algorithm

Invasive weed optimization algorithm is a new swarm intelligence algorithm recently. The algorithm has better robustness and adaptation, which is a very good intelligent optimization tools; but it is easy to fall into local optimization, and having the low speed of convergence, and it can not acquire exactly. Aiming at the shortcomings of the algorithm, taking advantage of pattern search excellent local search ability, this paper presents a novel hybrid optimization algorithm of pattern search algorithm and IWO optimization. The Simulation results of three standard benchmark functions show that the improved algorithm can greatly improve the convergence precision and convergence speed, and can effectively discourage the premature convergence.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Sign in / Sign up

Export Citation Format

Share Document