Bayesian Interactive Search Algorithm: A New Probabilistic Swarm Intelligence Tested on Mathematical and Structural Optimization Problems

2021 ◽  
Vol 155 ◽  
pp. 102994
Author(s):  
Ali Mortazavi
Keyword(s):  
Structural Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Interactive Search

Structural Optimization With Nonlinear Goal Programming Using Powell’s Method

1991 ◽  
Author(s):  
Mohamed E. M. El-Sayed ◽  
T. S. Jang
Keyword(s):  
Structural Optimization ◽  
Goal Programming ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Numerical Test ◽  
Design Tool ◽  
Test Cases ◽  
Programming Techniques ◽  
Ordered Design

Abstract This paper presents a method for solving structural optimization problems using nonlinear goal programming techniques. The developed method removes the difficulty of having to define an objective function and constraints. It also has the capacity of handling rank ordered design objectives or goals. The formulation of the structural optimization problem into a goal programming form is discussed. The resulting optimization problem is solved using Powell’s conjugate direction search algorithm. To demonstrate the effectiveness of the method, as a design tool, the solutions of some numerical test cases are included.

Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

2011 ◽  
Vol 29 (1) ◽  
pp. 17-35 ◽  
Author(s):  
Amir Hossein Gandomi ◽  
Xin-She Yang ◽  
Amir Hossein Alavi
Keyword(s):  
Structural Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

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.

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