Interactive Fuzzy Bayesian Search Algorithm: A New Reinforced Swarm Intelligence Tested on Engineering and Mathematical Optimization Problems

2021 ◽  
pp. 115954
Author(s):  
Ali Mortazavi
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Mathematical Optimization

scholarly journals Applications of Harmony Search Algorithm

10.29007/pvrk ◽  
2018 ◽  
Author(s):  
Gunjan Chauhan ◽  
Vishal Patel ◽  
Vishal Arekar
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Mathematical Optimization ◽  
Visual Basic ◽  
Harmony Search Algorithm ◽  
Particle Swarm Algorithm ◽  
Decision Variables ◽  
Optimum Solution

Harmony search Algorithm (HSA) is mostly preferred for solving optimizationproblems and gives optimum solution of the problems. . It is based on improvisation of harmony in music process where musicians improvise their instruments’ pitch by searching for a aesthetically pleasant harmony. As the musicians in improvisation process try to find the best harmony in terms of aesthetics, the decision variables in optimization process try to be the best solution in terms of objective function. In the present work, the harmony search method is studied with an attempt to use it to solve various structural optimization problems. Harmony search can be more effective than some of the optimization available right now like genetic algorithms, particle swarm algorithm, ant colony algorithm, gravity search algorithm etc. The programming language used in this work is Visual Basic-macro excel. The programs for harmony search algorithm is developed in macro and their reliability is checked by verifying it with various mathematical optimization problems.

An Application of Cuckoo Search Algorithm for Solving Optimal Chiller Loading Problem for Energy Conservation

2015 ◽  
Vol 793 ◽  
pp. 500-504 ◽  
Author(s):  
Mohd Herwan Sulaiman ◽  
Muhammad Ikram Mohd Rashid ◽  
Mohd Rusllim Mohamed ◽  
Omar Aliman ◽  
Hamdan Daniyal
Keyword(s):  
Energy Conservation ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Load Ratio ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Industrial Facilities ◽  
Partial Load ◽  
Loading Problem

This paper presents a recent swarm intelligence technique viz. Cuckoo Search Algorithm (CSA) for solving the Optimal Chiller Loading (OCL) problem for energy conservation. Multi-chillers system has been widely used by commercial and industrial facilities to provide cooling energy. The main problem of multi-chillers system is that it conserved huge amount of energy. In this study, Partial Load Ratio (PLR) of the chiller is used as the variables to be optimized while the power consumption in kW is selected as the objective function to be minimized. On the other hand, CSA is a one of well-known swarm intelligence techniques that has been used to solve many optimization problems. In order to show the effectiveness of CSA in solving OCL problem, a case study with six-chiller system is considered. Results obtained are compared with other techniques available in literatures.

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.

A Non-Monotone Line Search Combination Technique for Unconstrained Optimization

2014 ◽  
Vol 8 (1) ◽  
pp. 218-221 ◽  
Author(s):  
Ping Hu ◽  
Zong-yao Wang
Keyword(s):  
Global Convergence ◽  
Unconstrained Optimization ◽  
Numerical Experiments ◽  
Line Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
New Combination ◽  
Combination Rule ◽  
Combination Technique ◽  
Unconstrained Optimization Problems

We propose a non-monotone line search combination rule for unconstrained optimization problems, the corresponding non-monotone search algorithm is established and its global convergence can be proved. Finally, we use some numerical experiments to illustrate the new combination of non-monotone search algorithm’s effectiveness.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

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