Two Meta-Heuristics for Solving Unconstrained Optimization Problems and Machinery Problems

2014 ◽  
Vol 1044-1045 ◽  
pp. 1418-1423
Author(s):  
Pasura Aungkulanon
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Finite Sequence ◽  
Swarm Optimization ◽  
Machining Optimization

Machining optimization problem aims to optimize machinery conditions which are important for economic settings. The effective methods for solving these problems using a finite sequence of instructions can be categorized into two groups; exact optimization algorithm and meta-heuristic algorithms. A well-known meta-heuristic approach called Harmony Search Algorithm was used to compare with Particle Swarm Optimization. We implemented and analysed algorithms using unconstrained problems under different conditions included single, multi-peak, curved ridge optimization, and machinery optimization problem. The computational outputs demonstrated the proposed Particle Swarm Optimization resulted in the better outcomes in term of mean and variance of process yields.

scholarly journals Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.  

scholarly journals The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 862 ◽  
Author(s):  
José García ◽  
José V. Martí ◽  
Víctor Yepes
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Retaining Walls ◽  
Harmony Search Algorithm ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Reinforced Earth

The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

Test Case Optimization and Prioritization Using Improved Cuckoo Search and Particle Swarm Optimization Algorithm

2018 ◽  
Vol 7 (4.6) ◽  
pp. 275
Author(s):  
Chandrasekhara Reddy T ◽  
Srivani V ◽  
A. Mallikarjuna Reddy ◽  
G. Vishnu Murthy
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Suite ◽  
Swarm Optimization ◽  
Cuckoo Algorithm

For minimized t-way test suite generation (t indicates more strength of interaction) recently many meta-heuristic, hybrid and hyper-heuristic algorithms are proposed which includes Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Genetic Algorithms (GA), Simulated Annealing (SA), Cuckoo Search (CS), Harmony Elements Algorithm (HE), Exponential Monte Carlo with counter (EMCQ), Particle Swarm Optimization (PSO), and Choice Function (CF). Although useful strategies are required specific domain knowledge to allow effective tuning before good quality solutions can be obtained. In our proposed technique test cases are optimized by utilizing Improved Cuckoo Algorithm (ICSA). At that point, the advanced experiments are organized or prioritized by utilizing Particle Swarm Optimization algorithm (PSO). The Particle Swarm Optimization and Improved Cuckoo Algorithm (PSOICSA) estimation is a blend of Improved Cuckoo Search Algorithm(ICSA) and Particle Swarm Optimization (PSO). PSOICSA could be utilized to advance the test suite, and coordinate both ICSA and PSO for a superior outcome, when contrasted with their individual execution as far as experiment improvement. 

scholarly journals Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 827 ◽  
Author(s):  
E. J. Solteiro Pires ◽  
J. A. Tenreiro Machado ◽  
P. B. de Moura Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multiobjective Particle Swarm Optimization ◽  
Multiobjective Algorithms ◽  
Average Fitness

Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

scholarly journals A Novel Multiobjective Quantum-Behaved Particle Swarm Optimization Based on the Ring Model

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Di Zhou ◽  
Yajun Li ◽  
Bin Jiang ◽  
Jun Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Population Based ◽  
Swarm Optimization ◽  
Ring Model ◽  
Global Optima ◽  
Searching Ability ◽  
Update Strategy

Due to its fast convergence and population-based nature, particle swarm optimization (PSO) has been widely applied to address the multiobjective optimization problems (MOPs). However, the classical PSO has been proved to be not a global search algorithm. Therefore, there may exist the problem of not being able to converge to global optima in the multiobjective PSO-based algorithms. In this paper, making full use of the global convergence property of quantum-behaved particle swarm optimization (QPSO), a novel multiobjective QPSO algorithm based on the ring model is proposed. Based on the ring model, the position-update strategy is improved to address MOPs. The employment of a novel communication mechanism between particles effectively slows down the descent speed of the swarm diversity. Moreover, the searching ability is further improved by adjusting the position of local attractor. Experiment results show that the proposed algorithm is highly competitive on both convergence and diversity in solving the MOPs. In addition, the advantage becomes even more obvious with the number of objectives increasing.

Significant Factors Identification for Particle Swarm Optimization Algorithm to Solving the Design Optimization Problem of a Four-Bar Linkage for Path Generation

2012 ◽  
Vol 249-250 ◽  
pp. 1180-1187 ◽  
Author(s):  
Cheng Kang Lee ◽  
Yung Chang Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Based ◽  
Path Generation ◽  
Swarm Optimization ◽  
Control Factors ◽  
Four Bar Linkage

Particle swarm optimization (PSO) is a well-known population-based searching algorithm to solving optimization problems. This paper aims at identifying significant control factors for PSO to solving the design optimization problem of a four-bar linkage for path generation. Control factors considered herein are inertial weight, acceleration coefficients, breeding operation, and the number of population. A full factorial design of experiments is used to construct a set of experiments. Experimental results are analyzed with the analysis of variance method. According to the results obtained in this paper, breeding operation and the interaction between breeding operation and acceleration coefficients are significant. Inertial weight, acceleration coefficients, the number of population, and the other interactions are not significant. For the design optimization problem discussed herein, it is suggested to adopt breeding operation strategy and apply constant acceleration coefficients to increase significantly PSO’s performance and robustness. Type of inertial weight and the number of population do not affect PSO’s performance and robustness significantly.

Harmony Search Algorithm and its Variants

2015 ◽  
Vol 29 (08) ◽  
pp. 1539001 ◽  
Author(s):  
Nazmul Siddique ◽  
Hojjat Adeli
Keyword(s):  
Heuristic Search ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Population Based ◽  
Harmony Search Algorithm ◽  
Perfect State ◽  
The Past

In the past three decades nature-inspired and meta-heuristic algorithms have dominated the literature in the broad areas of search and optimization. Harmony search algorithm (HSA) is a music-inspired population-based meta-heuristic search and optimization algorithm. The concept behind the algorithm is to find a perfect state of harmony determined by aesthetic estimation. This paper starts with an overview of the harmonic phenomenon in music and music improvisation used by musicians and how it is applied to the optimization problem. The concept of harmony memory and its mathematical implementation are introduced. A review of HSA and its variants is presented. Guidelines from the literature on the choice of parameters used in HSA for effective solution of optimization problems are summarized.

Sign in / Sign up

Export Citation Format

Share Document