Analysis of Convergence for Free Search Algorithm in Solving Complex Function Optimization Problems

2011 ◽  
pp. 1201-1207
Author(s):  
Lu Li ◽  
Zihou Zhang ◽  
Xingyu Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Complex Function ◽  
Function Optimization ◽  
Free Search

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

Model Selecting PSO-FA Hybrid for Complex Function Optimization

2021 ◽  
Vol 12 (3) ◽  
pp. 215-232
Author(s):  
Heng Xiao ◽  
Toshiharu Hatanaka
Keyword(s):  
Model Selection ◽  
Swarm Intelligence ◽  
Hybrid Algorithm ◽  
Optimization Problems ◽  
Complex Function ◽  
Group Behavior ◽  
Black Box ◽  
Function Optimization ◽  
Selection Strategy ◽  
Natural Group

Swarm intelligence is inspired by natural group behavior. It is one of the promising metaheuristics for black-box function optimization. Then plenty of swarm intelligence algorithms such as particle swarm optimization (PSO) and firefly algorithm (FA) have been developed. Since these swarm intelligence models have some common properties and inherent characteristics, model hybridization is expected to adjust a swarm intelligence model for the target problem instead of parameter tuning that needs some trial and error approach. This paper proposes a PSO-FA hybrid algorithm with a model selection strategy. An event-driven trigger based on the personal best update makes each individual do the model selection that focuses on the personal study process. By testing the proposed hybrid algorithm on some benchmark problems and comparing it with a simple PSO, the standard PSO 2011, FA, HFPSO to show how the proposed hybrid swarm averagely performs well in black-box optimization problems.

A Heuristic Algorithm Based on Leadership Strategy: Leader of Dolphin Herd Algorithm (LDHA)

2015 ◽  
Vol 19 (4) ◽  
pp. 491-499 ◽  
Author(s):  
Jianqiang Zhao ◽  
◽  
Kao Ge ◽  
Kangyao Xu
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Complex Function ◽  
Predatory Behavior ◽  
Function Optimization ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Leadership Strategy ◽  
Complex Functions

A heuristic algorithm named the leader of dolphin herd algorithm (LDHA) is proposed in this paper to solve an optimization problem whose dimensionality is not high, with dolphins that imitate predatory behavior. LDHA is based on a leadership strategy. Using the leadership strategy as reference, we have designed the proposed algorithm by simulating the preying actions of dolphin herds. Several intelligent behaviors, such as “producing leaders,” “group gathering,” “information sharing,” and “rounding up prey,” are abstracted by LDHA. The proposed algorithm is tested on 15 typical complex function optimization problems. The testing results reveal that compared with the particle swarm optimization and the genetic algorithms, LDHA has relatively high optimization accuracy and capability for complex functions. Further, it is almost unaffected by the inimicality, multimodality, or dimensions of functions in the function optimization section, which implies better convergence. In addition, ultra-high-dimensional function optimization capabilities of this algorithm were tested using the IEEE CEC 2013 global optimization benchmark. Unfortunately, the proposed optimization algorithm has a limitation in that it is not suitable for ultra-high-dimensional functions.

An improved differential harmony search algorithm for function optimization problems

2018 ◽  
Vol 23 (13) ◽  
pp. 4827-4852 ◽  
Author(s):  
Lin Wang ◽  
Huanling Hu ◽  
Rui Liu ◽  
Xiaojian Zhou
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization

scholarly journals A Hybrid Lightning Search Algorithm-Simplex Method for Global Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-23 ◽  
Author(s):  
Yuting Lu ◽  
Yongquan Zhou ◽  
Xiuli Wu
Keyword(s):  
Engineering Design ◽  
Simplex Method ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Function Optimization ◽  
Local Optimum ◽  
Computational Accuracy ◽  
Design Problems ◽  
Opposition Based Learning ◽  
Engineering Design Problems

In this paper, a novel hybrid lightning search algorithm-simplex method (LSA-SM) is proposed to solve the shortcomings of lightning search algorithm (LSA) premature convergence and low computational accuracy and it is applied to function optimization and constrained engineering design optimization problems. The improvement adds two major optimization strategies. Simplex method (SM) iteratively optimizes the current worst step leaders to avoid the population searching at the edge, thus improving the convergence accuracy and rate of the algorithm. Elite opposition-based learning (EOBL) increases the diversity of population to avoid the algorithm falling into local optimum. LSA-SM is tested by 18 benchmark functions and five constrained engineering design problems. The results show that LSA-SM has higher computational accuracy, faster convergence rate, and stronger stability than other algorithms and can effectively solve the problem of constrained nonlinear optimization in reality.

Research of Parallel Artificial Bee Colony Algorithm Based on MPI

2013 ◽  
Vol 380-384 ◽  
pp. 1430-1433 ◽  
Author(s):  
Ying Sen Hong ◽  
Zhen Zhou Ji ◽  
Chun Lei Liu
Keyword(s):  
Parallel Algorithm ◽  
Message Passing ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Complex Function ◽  
Function Optimization ◽  
Search Performance ◽  
Bee Colony ◽  
Long Time

Artificial bee colony algorithm is a smart optimization algorithm based on the bees acquisition model. A long time for the search of the artificial bee colony algorithm, in this paper we propose a parallel algorithm of artificial bee colony algorithm (MPI-ABC), with an application of a parallel programming environment MPI, using the programming mode of message passing rewriting the serial algorithm in parallel. Finally, this paper compare both serial and parallel algorithm with testing on complex function optimization problems. The experimental results show that the algorithm is effective to improve the search performance, especially for high-dimensional complex optimization problem.

scholarly journals A Cooperative Harmony Search Algorithm for Function Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Gang Li ◽  
Qingzhong Wang
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cooperative Behavior ◽  
Optimization Technique ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Function Optimization ◽  
Solution Vector ◽  
Original Algorithm ◽  
Harmony Memory

Harmony search algorithm (HS) is a new metaheuristic algorithm which is inspired by a process involving musical improvisation. HS is a stochastic optimization technique that is similar to genetic algorithms (GAs) and particle swarm optimizers (PSOs). It has been widely applied in order to solve many complex optimization problems, including continuous and discrete problems, such as structure design, and function optimization. A cooperative harmony search algorithm (CHS) is developed in this paper, with cooperative behavior being employed as a significant improvement to the performance of the original algorithm. Standard HS just uses one harmony memory and all the variables of the object function are improvised within the harmony memory, while the proposed algorithm CHS uses multiple harmony memories, so that each harmony memory can optimize different components of the solution vector. The CHS was then applied to function optimization problems. The results of the experiment show that CHS is capable of finding better solutions when compared to HS and a number of other algorithms, especially in high-dimensional problems.

Sign in / Sign up

Export Citation Format

Share Document