scholarly journals Predatory Search Strategy Based on Swarm Intelligence for Continuous Optimization Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
J. W. Wang ◽  
H. F. Wang ◽  
W. H. Ip ◽  
K. Furuta ◽  
T. Kanno ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Search Strategy ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Pso Algorithm ◽  
Continuous Variable ◽  
Test Functions ◽  
Continuous Optimization Problems ◽  
Local Optimizer ◽  
Intelligence Technique

We propose an approach to solve continuous variable optimization problems. The approach is based on the integration of predatory search strategy (PSS) and swarm intelligence technique. The integration is further based on two newly defined concepts proposed for the PSS, namely, “restriction” and “neighborhood,” and takes the particle swarm optimization (PSO) algorithm as the local optimizer. The PSS is for the switch of exploitation and exploration (in particular by the adjustment of neighborhood), while the swarm intelligence technique is for searching the neighborhood. The proposed approach is thus named PSS-PSO. Five benchmarks are taken as test functions (including both unimodal and multimodal ones) to examine the effectiveness of the PSS-PSO with the seven well-known algorithms. The result of the test shows that the proposed approach PSS-PSO is superior to all the seven algorithms.

scholarly journals A New Quadratic Binary Harris Hawk Optimization for Feature Selection

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1130 ◽  
Author(s):  
Too ◽  
Abdullah ◽  
Mohd Saad
Keyword(s):  
Feature Selection ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Variable ◽  
Classification Performance ◽  
Flower Pollination Algorithm ◽  
Feature Selection Problem ◽  
Binary Differential Evolution ◽  
Continuous Optimization Problems ◽  
Selection Algorithms

Harris hawk optimization (HHO) is one of the recently proposed metaheuristic algorithms that has proven to be work more effectively in several challenging optimization tasks. However, the original HHO is developed to solve the continuous optimization problems, but not to the problems with binary variables. This paper proposes the binary version of HHO (BHHO) to solve the feature selection problem in classification tasks. The proposed BHHO is equipped with an S-shaped or V-shaped transfer function to convert the continuous variable into a binary one. Moreover, another variant of HHO, namely quadratic binary Harris hawk optimization (QBHHO), is proposed to enhance the performance of BHHO. In this study, twenty-two datasets collected from the UCI machine learning repository are used to validate the performance of proposed algorithms. A comparative study is conducted to compare the effectiveness of QBHHO with other feature selection algorithms such as binary differential evolution (BDE), genetic algorithm (GA), binary multi-verse optimizer (BMVO), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA). The experimental results show the superiority of the proposed QBHHO in terms of classification performance, feature size, and fitness values compared to other algorithms.

Parallel Line-Up Competition Algorithm for Continuous Optimization

2010 ◽  
Vol 20-23 ◽  
pp. 161-166 ◽  
Author(s):  
Bin Shi ◽  
Lie Xiang Yan ◽  
Hui Xie
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Parallel Line ◽  
Superior Performance ◽  
Coarse Grained ◽  
Test Functions ◽  
Neighboring Node ◽  
Simulation Results ◽  
Continuous Optimization Problems ◽  
Single Processor

This paper presents a new parallel line-up competition algorithm (PLCA) for solving continuous optimization problems. A coarse-grained parallel model has been used to implement the PLCA. In this model, the whole family is equally divided into several subfamily groups that evolve independently in single processor. When migration condition is reached, a portion of fit families in each subfamily group migrate to the neighboring node according to a ring migration topology. The proposed algorithm has been evaluated by solving a set of well-known test functions. Simulation results show that PLCA has good parallel efficiency and superior performance.

scholarly journals Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Li ◽  
Yifei Sun ◽  
Sicheng Hou
Keyword(s):  
Experimental Study ◽  
Particle Swarm Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fixed Phase ◽  
Continuous Optimization Problems

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.

A Study of the Continuous Optimization Problem Using a Wood Robot Controlled by a Biologically Motivated System

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
Jong-Chen Chen
Keyword(s):  
Physical Environment ◽  
Intelligent System ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Continuous Optimization ◽  
Physical Structure ◽  
Experimental Results ◽  
Self Organized ◽  
Selection Operators ◽  
Continuous Optimization Problems

Continuous optimization plays an increasingly significant role in everyday decision-making situations. Our group had previously developed a multilevel system called the artificial neuromolecular system (ANM) that possessed structure richness allowing variation and/or selection operators to act on it in order to generate a broad range of dynamic behaviors. In this paper, we used the ANM system to control the motions of a wooden walking robot named Miky. The robot was used to investigate the ANM system's capability to deal with continuous optimization problems through self-organized learning. Evolutionary learning algorithm was used to train the system and generate appropriate control. The experimental results showed that Miky was capable of learning in a continued manner in a physical environment. A further experiment was conducted by making some changes to Miky's physical structure in order to observe the system's capability to deal with the change. Detailed analysis of the experimental results showed that Miky responded to the change by appropriately adjusting its leg movements in space and time. The results showed that the ANM system possessed continuous optimization capability in coping with the change. Our findings from the empirical experiments might provide us another dimension of information of how to design an intelligent system comparatively friendlier than the traditional systems in assisting humans to walk.

scholarly journals A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

2020 ◽  
Vol 34 (05) ◽  
pp. 7111-7118
Author(s):  
Moumita Choudhury ◽  
Saaduddin Mahmud ◽  
Md. Mosaddek Khan
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Continuous Variables ◽  
Constraint Optimization ◽  
Solution Quality ◽  
Distributed Constraint Optimization ◽  
Continuous Optimization Problems ◽  
Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of several distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that can explicitly model a problem containing continuous variables. Nevertheless, state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.

scholarly journals A review of swarm intelligence algorithms deployment for scheduling and optimization in cloud computing environments

2021 ◽  
Vol 7 ◽  
pp. e696
Author(s):  
Yousef Qawqzeh ◽  
Mafawez T. Alharbi ◽  
Ayman Jaradat ◽  
Khalid Nazim Abdul Sattar
Keyword(s):  
Cloud Computing ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Collective Intelligence ◽  
Pso Algorithm ◽  
Cloud Computing Environment ◽  
Bee Colony ◽  
Self Organized ◽  
Intelligent Behavior ◽  
New Si

Background This review focuses on reviewing the recent publications of swarm intelligence algorithms (particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony (ABC), and the firefly algorithm (FA)) in scheduling and optimization problems. Swarm intelligence (SI) can be described as the intelligent behavior of natural living animals, fishes, and insects. In fact, it is based on agent groups or populations in which they have a reliable connection among them and with their environment. Inside such a group or population, each agent (member) performs according to certain rules that make it capable of maximizing the overall utility of that certain group or population. It can be described as a collective intelligence among self-organized members in certain group or population. In fact, biology inspired many researchers to mimic the behavior of certain natural swarms (birds, animals, or insects) to solve some computational problems effectively. Methodology SI techniques were utilized in cloud computing environment seeking optimum scheduling strategies. Hence, the most recent publications (2015–2021) that belongs to SI algorithms are reviewed and summarized. Results It is clear that the number of algorithms for cloud computing optimization is increasing rapidly. The number of PSO, ACO, ABC, and FA related journal papers has been visibility increased. However, it is noticeably that many recently emerging algorithms were emerged based on the amendment on the original SI algorithms especially the PSO algorithm. Conclusions The major intention of this work is to motivate interested researchers to develop and innovate new SI-based solutions that can handle complex and multi-objective computational problems.

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