The Cheapest Shop Seeker : A New Algorithm For Optimization Problem in a Continous Space

2016 ◽  
Vol 5 (3) ◽  
pp. 119
Author(s):  
Peter Bamidele Shola
Keyword(s):  
Success Rate ◽  
Rate Of Convergence ◽  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Population Based ◽  
Global Optimum ◽  
Optimum Point ◽  
Number Of Iterations

<div class="Section1"><p>In this paper a population-based meta-heuristic algorithm for optimization problems in a continous space is presented.The algorithm,here called cheapest shop seeker is modeled after a group of shoppers seeking to identify the cheapest shop (among many available) for shopping. The  algorithm was tested on many benchmark functions with the result  compared with those from some other methods. The algorithm appears to  have a better  success  rate of hitting the global optimum point  of a function  and of the rate of convergence (in terms of the number of iterations required to reach the optimum  value) for some functions  in spite  of its simplicity.</p></div>

An Algorithm for Continuous Optimization Problems using Hybrid Particle Updating Method

2016 ◽  
Vol 3 (1) ◽  
pp. 164
Author(s):  
Peter Bamidele Shola ◽  
L B Asaju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Population Based ◽  
Global Optimum ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Continuous Optimization Problems ◽  
Number Of Iterations

<p>Optimization problem is one such problem commonly encountered in many area of endeavor, obviously due to the need to economize the use of the available resources in many problems. This paper presents a population-based meta-heuristic algorithm   for solving optimization problems in a continous space. The algorithm, combines a form of cross-over technique with a position updating formula based on the instantaneous global best position to update each particle position .The algorithm was tested and compared with the standard particle swarm optimization (PSO)  on many benchmark functions. The result suggests a better performance of the algorithm over the later in terms of reaching (attaining) the global optimum value (at least for those benchmark functions considered) and the rate of convergence in terms of the number of iterations required reaching the optimum values.</p>

Solving Mono- and Multi-Objective Problems Using Hybrid Evolutionary Algorithms and Nelder-Mead Method

2021 ◽  
Vol 12 (4) ◽  
pp. 98-116
Author(s):  
Noureddine Boukhari ◽  
Fatima Debbat ◽  
Nicolas Monmarché ◽  
Mohamed Slimane
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Optimization Methods ◽  
Global Optimum ◽  
Stochastic Methods ◽  
Local Optima ◽  
Portfolio Optimization Problem

Evolution strategies (ES) are a family of strong stochastic methods for global optimization and have proved their capability in avoiding local optima more than other optimization methods. Many researchers have investigated different versions of the original evolution strategy with good results in a variety of optimization problems. However, the convergence rate of the algorithm to the global optimum stays asymptotic. In order to accelerate the convergence rate, a hybrid approach is proposed using the nonlinear simplex method (Nelder-Mead) and an adaptive scheme to control the local search application, and the authors demonstrate that such combination yields significantly better convergence. The new proposed method has been tested on 15 complex benchmark functions and applied to the bi-objective portfolio optimization problem and compared with other state-of-the-art techniques. Experimental results show that the performance is improved by this hybridization in terms of solution eminence and strong convergence.

Glowworm Swarm Optimization (GSO) Algorithm for Optimization Problems: A State-of-the-Art Review

2013 ◽  
Vol 421 ◽  
pp. 507-511 ◽  
Author(s):  
Nurezayana Zainal ◽  
Azlan Mohd Zain ◽  
Nor Haizan Mohamed Radzi ◽  
Amirmudin Udin
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Global Optimum ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Multimodal Function Optimization ◽  
Glowworm Swarm Optimization ◽  
Multimodal Function ◽  
Optimum Solution

Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.

A New Simple Micro-PSO for High Dimensional Optimization Problem

2012 ◽  
Vol 236-237 ◽  
pp. 1195-1200
Author(s):  
Wen Hua Han
Keyword(s):  
Optimization Problem ◽  
Optimization Technique ◽  
Pso Algorithm ◽  
Population Based ◽  
Global Optimum ◽  
Stochastic Search ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Search Optimization ◽  
Dimensional Optimization

The particle swarm optimization (PSO) algorithm is a population-based intelligent stochastic search optimization technique, which has already been widely used to various of fields. In this paper, a simple micro-PSO is proposed for high dimensional optimization problem, which is resulted from being introduced escape boundary and perturbation for global optimum. The advantages of the simple micro-PSO are more simple and easily implemented than the previous micro-PSO. Experiments were conducted using Griewank, Rosenbrock, Ackley, Tablets functions. The experimental results demonstrate that the simple micro-PSO are higher optimization precision and faster convergence rate than PSO and robust for the dimension of the optimization problem.

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.

Ultimate and Practical Limits for Counterweight Balancing of Six-Bar Linkages

2006 ◽  
Author(s):  
Bram Demeulenaere ◽  
Jan Swevers ◽  
Joris De Schutter
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Global Optimum ◽  
Great Efficiency ◽  
Convex Programs ◽  
Local Optima ◽  
Trade Off ◽  
Convex Constraint ◽  
Main Challenge ◽  
Dynamic Forces

The designer’s main challenge when counterweight balancing a linkage is to determine the counterweights that realize an optimal trade-off between the dynamic forces of interest. This problem is often formulated as an optimization problem that is generally nonlinear and therefore suffers from local optima. It has been shown earlier, however, that, through a proper parametrization of the counterweights, a convex program can be obtained. Convex programs are nonlinear optimization problems of which the global optimum is guaranteed to be found with great efficiency. The present paper extends this previous work in two respects: (i) the methodology is generalized from four-bar to planar N-bar (rigid) linkages and (ii) it is shown that requiring the counterweights to be realizable in practice can be cast as a convex constraint. Numerical results for a Watt six-bar linkage suggest much more balancing potential for six-bar linkages than for four-bar linkages.

scholarly journals Harris Hawks Optimization for Optimum Design of Truss Structures with Discrete Variables

2021 ◽  
Vol 6 (4) ◽  
pp. 1157-1173
Author(s):  
Mustafa Al-Bazoon
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Population Based ◽  
Truss Structures ◽  
Discrete Variables ◽  
Structural Problems ◽  
Collaborative Behavior ◽  
Design Variables ◽  
Predatory Birds ◽  
The Mathematical Model

This article investigates the use of Harris Hawks Optimization (HHO) to solve planar and spatial trusses with design variables that are discrete. The original HHO has been used to solve continuous design variables problems. However, HHO is formulated to solve optimization problems with discrete variables in this research. HHO is a population-based metaheuristic algorithm that simulates the chasing style and the collaborative behavior of predatory birds Harris hawks. The mathematical model of HHO uses a straightforward formulation and does not require tuning of algorithmic parameters and it is a robust algorithm in exploitation. The performance of HHO is evaluated using five benchmark structural problems and the final designs are compared with ten state-of-the-art algorithms. The statistical outcomes (average and standard deviation of final designs) show that HHO is quite consistent and robust in solving truss structure optimization problems. This is an important characteristic that leads to better confidence in the final solution from a single run of the algorithm for an optimization problem.

Counterweight Balancing for Reducing Machine Frame Vibration: A Convex Optimization Framework

2006 ◽  
Author(s):  
Myriam Verschuure ◽  
Bram Demeulenaere ◽  
Jan Swevers ◽  
Joris De Schutter
Keyword(s):  
Convex Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Global Optimum ◽  
Convex Optimization Problem ◽  
Optimization Framework ◽  
Convex Optimization Problems ◽  
Drive Speed ◽  
Maximal Reduction

This paper focusses on reducing, through counterweight addition, the vibration of an elastically mounted, rigid machine frame that supports a linkage. In order to determine the counterweights that yield a maximal reduction in frame vibration, a non-linear optimization problem is formulated with the frame kinetic energy as objective function and such that a convex optimization problem is obtained. Convex optimization problems are nonlinear optimization problems that have a unique (global) optimum, which can be found with great efficiency. The proposed methodology is successfully applied to improve the results of the benchmark four-bar problem, first considered by Kochev and Gurdev. For this example, the balancing is shown to be very robust for drive speed variations and to benefit only marginally from using a coupler counterweight.

scholarly journals Hybrid Biogeography Based Optimization for Constrained Numerical and Engineering Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Zengqiang Mi ◽  
Yikun Xu ◽  
Yang Yu ◽  
Tong Zhao ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Constrained Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Population Based ◽  
Global Optimum ◽  
Engineering Optimization ◽  
Mutation Operator ◽  
Random Mutation ◽  
Promising Solution ◽  
Population Based Algorithm

Biogeography based optimization (BBO) is a new competitive population-based algorithm inspired by biogeography. It simulates the migration of species in nature to share information. A new hybrid BBO (HBBO) is presented in the paper for constrained optimization. By combining differential evolution (DE) mutation operator with simulated binary crosser (SBX) of genetic algorithms (GAs) reasonably, a new mutation operator is proposed to generate promising solution instead of the random mutation in basic BBO. In addition, DE mutation is still integrated to update one half of population to further lead the evolution towards the global optimum and the chaotic search is introduced to improve the diversity of population. HBBO is tested on twelve benchmark functions and four engineering optimization problems. Experimental results demonstrate that HBBO is effective and efficient for constrained optimization and in contrast with other state-of-the-art evolutionary algorithms (EAs), the performance of HBBO is better, or at least comparable in terms of the quality of the final solutions and computational cost. Furthermore, the influence of the maximum mutation rate is also investigated.

scholarly journals Lévy-Flight Krill Herd Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Gaige Wang ◽  
Lihong Guo ◽  
Amir Hossein Gandomi ◽  
Lihua Cao ◽  
Amir Hossein Alavi ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Computing Time ◽  
Optimization Methods ◽  
Population Based ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Krill Herd Algorithm ◽  
Krill Herd ◽  
Elitism Scheme

To improve the performance of the krill herd (KH) algorithm, in this paper, a Lévy-flight krill herd (LKH) algorithm is proposed for solving optimization tasks within limited computing time. The improvement includes the addition of a new local Lévy-flight (LLF) operator during the process when updating krill in order to improve its efficiency and reliability coping with global numerical optimization problems. The LLF operator encourages the exploitation and makes the krill individuals search the space carefully at the end of the search. The elitism scheme is also applied to keep the best krill during the process when updating the krill. Fourteen standard benchmark functions are used to verify the effects of these improvements and it is illustrated that, in most cases, the performance of this novel metaheuristic LKH method is superior to, or at least highly competitive with, the standard KH and other population-based optimization methods. Especially, this new method can accelerate the global convergence speed to the true global optimum while preserving the main feature of the basic KH.

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