scholarly journals Optimization of High-Dimensional Functions through Hypercube Evaluation

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rahib H. Abiyev ◽  
Mustafa Tunay
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Evaluation Process ◽  
Stochastic Search ◽  
High Dimensional ◽  
Potential Candidate ◽  
Objective Functions ◽  
Global Numerical Optimization ◽  
Search Area

A novel learning algorithm for solving global numerical optimization problems is proposed. The proposed learning algorithm is intense stochastic search method which is based on evaluation and optimization of a hypercube and is called the hypercube optimization (HO) algorithm. The HO algorithm comprises the initialization and evaluation process, displacement-shrink process, and searching space process. The initialization and evaluation process initializes initial solution and evaluates the solutions in given hypercube. The displacement-shrink process determines displacement and evaluates objective functions using new points, and the search area process determines next hypercube using certain rules and evaluates the new solutions. The algorithms for these processes have been designed and presented in the paper. The designed HO algorithm is tested on specific benchmark functions. The simulations of HO algorithm have been performed for optimization of functions of 1000-, 5000-, or even 10000 dimensions. The comparative simulation results with other approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low and high dimensional functions.

A Hybrid Multi-Agent-Based BFPSO Algorithm for Optimization of Benchmark Functions

2019 ◽  
Vol 29 (07) ◽  
pp. 2050112
Author(s):  
Renuka Kamdar ◽  
Priyanka Paliwal ◽  
Yogendra Kumar
Keyword(s):  
Optimization Problems ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
System Optimization ◽  
High Dimensional ◽  
Potential Candidate ◽  
Optimization Strategy ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Multi Agent

The goal to provide faster and optimal solution to complex and high-dimensional problem is pushing the technical envelope related to new algorithms. While many approaches use centralized strategies, the concept of multi-agent systems (MASS) is creating a new option related to distributed analyses for the optimization problems. A novel learning algorithm for solving the global numerical optimization problems is proposed. The proposed learning algorithm integrates the multi-agent system and the hybrid butterfly–particle swarm optimization (BFPSO) algorithm. Thus it is named as multi-agent-based BFPSO (MABFPSO). In order to obtain the optimal solution quickly, each agent competes and cooperates with its neighbors and it can also learn by using its knowledge. Making use of these agent–agent interactions and sensitivity and probability mechanism of BFPSO, MABFPSO realizes the purpose of optimizing the value of objective function. The designed MABFPSO algorithm is tested on specific benchmark functions. Simulations of the proposed algorithm have been performed for the optimization of functions of 2, 20 and 30 dimensions. The comparative simulation results with conventional PSO approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low-and high-dimensional functions. The optimization strategy is general and can be used to solve other power system optimization problems as well.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

scholarly journals GBUO: “The Good, the Bad, and the Ugly” Optimizer

2021 ◽  
Vol 11 (5) ◽  
pp. 2042
Author(s):  
Hadi Givi ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ruben Morales-Menendez ◽  
Ricardo A. Ramirez-Mendoza ◽  
...  
Keyword(s):  
High Dimension ◽  
Essential Role ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Stochastic Search ◽  
Objective Functions ◽  
Science And Engineering ◽  
Fixed Dimension ◽  
Dimension Functions ◽  
Simulation Results

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms.

scholarly journals Heterogeneous Differential Evolution for Numerical Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hui Wang ◽  
Wenjun Wang ◽  
Zhihua Cui ◽  
Hui Sun ◽  
Shahryar Rahnamayan
Keyword(s):  
Differential Evolution ◽  
Numerical Optimization ◽  
Large Scale ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Population Based ◽  
Stochastic Search ◽  
Test Problems ◽  
Exploration And Exploitation ◽  
Large Scale Problems

Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE) to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems.

scholarly journals A Novel Hybrid Bat Algorithm with Harmony Search for Global Numerical Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Harmony Search ◽  
Bat Algorithm ◽  
Optimization Methods ◽  
Population Based ◽  
Optimization Approach ◽  
Global Numerical Optimization ◽  
Hybrid Metaheuristic ◽  
Real World Applications

A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.

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