scholarly journals Adjustable mode ratio and focus boost search strategy for cat swarm optimization

2021 ◽  
Vol 1 (1) ◽  
pp. 75-94
Author(s):  
Pei-Wei Tsai ◽  
◽  
Xingsi Xue ◽  
Jing Zhang ◽  
Vaci Istanda ◽  
...  
Keyword(s):  
Search Strategy ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Test Results ◽  
Test Functions ◽  
Swarm Optimization ◽  
Cat Swarm Optimization ◽  
Control Scheme ◽  
Overall Performance ◽  
Parameter Values

<abstract><p>Evolutionary algorithm is one of the optimization techniques. Cat swarm optimization (CSO)-based algorithm is frequently used in many applications for solving challenging optimization problems. In this paper, the tracing mode in CSO is modified to reduce the number of user-defined parameters and weaken the sensitivity to the parameter values. In addition, a <italic>mode ratio</italic> control scheme for switching individuals between different movement modes and a search boosting strategy are proposed. The obtained results from our method are compared with the modified CSO without the proposed strategy, the original CSO, the particle swarm optimization (PSO) and differential evolution (DE) with three commonly-used DE search schemes. Six test functions from IEEE congress on evolutionary competition (CEC) are used to evaluate the proposed methods. The overall performance is evaluated by the average ranking over all test results. The ranking result indicates that our proposed method outperforms the other methods compared.</p></abstract>

scholarly journals Comparative Study of Optimization Algorithms for The Optimal Reservoir Operation

2018 ◽  
Vol 246 ◽  
pp. 01003
Author(s):  
Xinyuan Liu ◽  
Yonghui Zhu ◽  
Lingyun Li ◽  
Lu Chen
Keyword(s):  
Reservoir Operation ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Complex Problem ◽  
Optimization Techniques ◽  
Decision Variables ◽  
Handling Methods ◽  
Optimality Algorithm ◽  
Parameter Values

Apart from traditional optimization techniques, e.g. progressive optimality algorithm (POA), modern intelligence algorithms, like genetic algorithms, differential evolution have been widely used to solve optimization problems. This paper deals with comparative analysis of POA, GA and DE and their applications in a reservoir operation problem. The results show that both GA and DES are feasible to reservoir operation optimization, but they display different features. GA and DE have many parameters and are difficult in determination of these parameter values. For simple problems with mall number of decision variables, GA and DE are better than POA when adopting appropriate parameter values and constraint handling methods. But for complex problem with large number of variables, POA combined with simplex method are much superior to GA and DE in time-assuming and quality of optimal solutions. This study helps to select proper optimization algorithms and parameter values in reservoir operation.

scholarly journals Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

2021 ◽  
Vol 21 (1) ◽  
pp. 62-72
Author(s):  
R. B. Madhumala ◽  
Harshvardhan Tiwari ◽  
Verma C. Devaraj
Keyword(s):  
Particle Swarm Optimization ◽  
Virtual Machine ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Virtual Machine Placement ◽  
Placement Problem ◽  
Swarm Optimization ◽  
Cloud Datacenter

Abstract Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

From Optimization to Clustering

2015 ◽  
pp. 594-619 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

From Optimization to Clustering

2016 ◽  
pp. 1519-1544 ◽  
Author(s):  
Megha Vora ◽  
T. T. Mirnalinee
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Efficient Solution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

scholarly journals An Improved Grey Wolf Optimizer for a Supplier Selection and Order Quantity Allocation Problem

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1457
Author(s):  
Avelina Alejo-Reyes ◽  
Erik Cuevas ◽  
Alma Rodríguez ◽  
Abraham Mendoza ◽  
Elias Olivares-Benitez
Keyword(s):  
Supplier Selection ◽  
Search Strategy ◽  
Optimization Problems ◽  
Displacement Vector ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Order Quantity ◽  
Hunting Behavior

Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating orders are modeled through a cost function that considers different elements, such as the price of raw materials, ordering costs, and holding costs. Obtaining the optimal solution for these models represents a complex problem due to their discontinuity, non-linearity, and high multi-modality. Under such conditions, it is not possible to use classical optimization methods. On the other hand, metaheuristic schemes have been extensively employed as alternative optimization techniques to solve difficult problems. Among the metaheuristic computation algorithms, the Grey Wolf Optimization (GWO) algorithm corresponds to a relatively new technique based on the hunting behavior of wolves. Even though GWO allows obtaining satisfying results, its limited exploration reduces its performance significantly when it faces high multi-modal and discontinuous cost functions. In this paper, a modified version of the GWO scheme is introduced to solve the complex optimization problems of supplier selection and order quantity allocation. The improved GWO method called iGWO includes weighted factors and a displacement vector to promote the exploration of the search strategy, avoiding the use of unfeasible solutions. In order to evaluate its performance, the proposed algorithm has been tested on a number of instances of a difficult problem found in the literature. The results show that the proposed algorithm not only obtains the optimal cost solutions, but also maintains a better search strategy, finding feasible solutions in all instances.

Lifecycle-Based Swarm Optimization for Constrained Problem of Engineering

2013 ◽  
Vol 281 ◽  
pp. 710-714 ◽  
Author(s):  
Zhuang Wei Yin ◽  
Hai Shen ◽  
Yu Fu Deng ◽  
Mo Zhang
Keyword(s):  
Life Cycle ◽  
Optimization Problems ◽  
Time Windows ◽  
Optimization Techniques ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Constrained Problem ◽  
Engineering Problems ◽  
Life Cycle Process

There are many constrained optimization problems in engineering. Bio-inspired optimization algorithms have been widely used to solve various engineering problems. This paper presents a novel optimization algorithm called Lifecycle-based Swarm Optimization, inspired by biology life cycle. LSO algorithm imitates biologic life cycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on a Vehicle Routing Problem with Time Windows for demonstration the effectiveness and stability. The results demonstrate remarkable performance of the LSO algorithm on chosen case when compared to two successful optimization techniques.

An Effective Chaotic Cultural-Based Particle Swarm Optimization for Constrained Engineering Design Problems

2010 ◽  
Vol 20-23 ◽  
pp. 64-69 ◽  
Author(s):  
Yong Quan Zhou ◽  
Lingzi Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Engineering Design ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Local Search Strategy

In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.

scholarly journals Lifecycle-Based Swarm Optimization Method for Numerical Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Hai Shen ◽  
Yunlong Zhu ◽  
Xiaodan Liang
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Individual Organism ◽  
Engineering Problems ◽  
Mechanical Design Problem

Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO). Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.

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