Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 304-308
Author(s):  
Lu Gang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Extreme Points ◽  
Function Optimization ◽  
Experimental Result ◽  
Genetic Optimization ◽  
Binary Coding ◽  
Coding Method ◽  
Peak Function ◽  
Better Than

In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.

Niche Genetic Algorithm Based on Sexual Reproduction and Multimodal Function Optimization Problem

2013 ◽  
pp. 8-16
Author(s):  
Yulong Tian ◽  
Tao Gao ◽  
Weifang Zhai ◽  
Yaying Hu ◽  
Xinfeng Li
Keyword(s):  
Genetic Algorithm ◽  
Sexual Reproduction ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Optimization ◽  
Function Optimization ◽  
Premature Convergence ◽  
Multimodal Functions ◽  
Multimodal Function Optimization ◽  
Multimodal Function

In this paper, a genetic algorithm with sexual reproduction and niche selection technology is proposed. Simple genetic algorithm has been successfully applied to many evolutionary optimization problems. But there is a problem of premature convergence for complex multimodal functions. To solve it, the frame and realization of niche genetic algorithm based on sexual reproduction are presented. Age and sexual structures are given to the individuals referring the sexual reproduction and “niche” phenomena, importing the niche selection technology. During age and sexual operators, different evolutionary parameters are given to the individuals with different age and sexual structures. As a result, this genetic algorithm can combat premature convergence and keep the diversity of population. The testing for Rastrigin function and Shubert function proves that the niche genetic algorithm based on sexual reproduction is effective.

Artificial Life Optimization Algorithm and Applications

2011 ◽  
pp. 423-449
Author(s):  
Bo-Suk Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Artificial Life ◽  
Optimization Problem ◽  
Global Optimum ◽  
Function Optimization ◽  
Global Function ◽  
Test Function ◽  
Optimum Solution ◽  
Artificial World

This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global function optimization problem. In the ALRT, the emergent colony is a fundamental mechanism to search the optimum solution and can be accomplished through the metabolism, movement and reproduction among artificial organisms which appear at the optimum locations in the artificial world. In this case, the optimum locations mean the optimum solutions in the optimization problem. Hence, the ALRT focuses on the searching for the optimum solution in the location of emergent colonies and can achieve more accurate global optimum. The optimization results using different types of test functions are presented to demonstrate the described approach successfully achieves optimum performance. The algorithm is also applied to the test function optimization and optimum design of short journal bearing as a practical application. The optimized results are compared with those of genetic algorithm and successive quadratic programming to identify the optimizing ability.

scholarly journals The Multiagent Planning Problem

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Tamás Kalmár-Nagy ◽  
Giovanni Giardini ◽  
Bendegúz Dezső Bak
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Problem Solver ◽  
Planning Problem ◽  
Genetic Operators ◽  
Total Distance ◽  
Modified Genetic Algorithm ◽  
Multiagent Planning ◽  
Simple Paths ◽  
Better Than

The classical Multiple Traveling Salesmen Problem is a well-studied optimization problem. Given a set ofngoals/targets andmagents, the objective is to findmround trips, such that each target is visited only once and by only one agent, and the total distance of these round trips is minimal. In this paper we describe the Multiagent Planning Problem, a variant of the classical Multiple Traveling Salesmen Problem: given a set ofngoals/targets and a team ofmagents,msubtours (simple paths) are sought such that each target is visited only once and by only one agent. We optimize for minimum time rather than minimum total distance; therefore the objective is to find the Team Plan in which the longest subtour is as short as possible (a min–max problem). We propose an easy to implement Genetic Algorithm Inspired Descent (GAID) method which evolves a set of subtours using genetic operators. We benchmarked GAID against other evolutionary algorithms and heuristics. GAID outperformed the Ant Colony Optimization and the Modified Genetic Algorithm. Even though the heuristics specifically developed for Multiple Traveling Salesmen Problem (e.g.,k-split, bisection) outperformed GAID, these methods cannot solve the Multiagent Planning Problem. GAID proved to be much better than an open-source Matlab Multiple Traveling Salesmen Problem solver.

Niche Brain Storm Optimization Algorithm for Multi-Peak Function Optimization

2014 ◽  
Vol 989-994 ◽  
pp. 1626-1630 ◽  
Author(s):  
Heng Jun Zhou ◽  
Ming Yan Jiang ◽  
Xian Ye Ben
Keyword(s):  
Optimization Algorithm ◽  
Population Diversity ◽  
Function Optimization ◽  
Excellent Performance ◽  
Brain Storm Optimization ◽  
Peak Function ◽  
Searching Ability ◽  
Swarm Intelligence Optimization ◽  
Global And Local ◽  
Better Than

Brain Storm Optimization (BSO) is a novel proposed swarm intelligence optimization algorithm which has a fast convergent speed. However, it is easy to trap into local optimal. In this paper, a new model based on niche technology, which is named Niche Brain Storm Optimization (NBSO), is proposed to overcome the shortcoming of BSO. Niche technology effectively prevents premature and maintains population diversity during the evolution process. NBSO shows excellent performance in searching global value and finding multiple global and local optimal solutions for the multi-peak problems. Several benchmark functions are introduced to evaluate its performance. Experimental results show that NBSO performs better than BSO in global searching ability and faster than Niche Genetic Algorithm (NGA) in finding peaks for multi-peak function.

scholarly journals MULTI-OBJECTIVE LAND USE OPTIMIZATION THROUGH PARALLEL PARTICLE SWARM ALGORITHM: CASE STUDY BABOLDASHT DISTRICT OF ISFAHAN, IRAN

2016 ◽  
Vol 10 (1) ◽  
pp. 42-49
Author(s):  
Alireza Sahebgharani
Keyword(s):  
Genetic Algorithm ◽  
Land Use ◽  
Land Use Planning ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective ◽  
Land Use Optimization ◽  
Land Types ◽  
Better Than

Land use planning seeks to divide land, the most valuable resource in the hands of planners, among different land types. During this process, various conflicting objectives are emerged which land use planners should prepare land use plans satisfying these objectives and deal with a large set of data and variable. For this reason, land use allocation is a multi-objective NP-hard optimization problem which is not solvable by the current exact methods. Therefore, solving land use optimization problem relies on the application of meta-heuristics. In this paper, a novel meta-heuristic named parallel particle swarm is developed to allocate seven land types (residential, commercial, cultural, educational, medical, sportive and green space) to Baboldasht district of Isfahan covered by 200 allocation cells with size 1000 m2 for maximizing compactness, compatibility and suitability objective functions. Afterwards, the outputs of the new developed algorithm are compared to the outputs of genetic algorithm. The results demonstrated that the parallel particle swarm is better than genetic algorithm in terms of both solution quality (1.35%) and algorithm efficiency (63.7%). The results also showed that the outputs achieved by both algorithms are better than the current state of land use distribution. Thus, the method represented in this paper can be used as a useful tool in the hands of urban planners and decision makers, and supports the land use planning process.

A Novel Fuzzy Inspired Bat Algorithm for Multidimensional Function Optimization Problem

2019 ◽  
Vol 8 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Syeda Shabnam Hasan ◽  
Rashida Rahman ◽  
Khurshida Akther Jahan ◽  
Shaheda Islam ◽  
Arshiful Islam Shadman ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Fuzzy Controller ◽  
Bat Algorithm ◽  
Function Optimization ◽  
Size Parameter ◽  
Step Size ◽  
Existing Problems ◽  
Candidate Solution ◽  
And Control ◽  
Better Than

This article introduces Fuzzy Inspired Bat Algorithm (FIBA), which is an improved variant of the original Bat algorithm. The novelty of FIBA lies in the integration of a fuzzy controller with the basic Bat algorithm that tries to bring balance between the degree of explorations and exploitations during the mutation operation. Another novelty of FIBA is the introduction of a step size parameter, maintained separately for every candidate solution, to customize and control the mix of explorative and exploitative operations around each candidate solution. FIBA is tested on a standard benchmark set that includes 10 complex, scalable, high dimensional functions. The results on benchmark functions reveal that FIBA can perform sufficiently well, and often better than the original Bat algorithm and another recently proposed improved Bat variant. Such improvements on the experimental results imply that the fuzzy technique adopted by FIBA might be effective on other existing problems as well, and hence demand further research and investigation.

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