Solving Unconstrained Global Optimization Problems via Hybrid Swarm Intelligence Approaches

Stochastic global optimization (SGO) algorithms such as the particle swarm optimization (PSO) approach have become popular for solving unconstrained global optimization (UGO) problems. The PSO approach, which belongs to the swarm intelligence domain, does not require gradient information, enabling it to overcome this limitation of traditional nonlinear programming methods. Unfortunately, PSO algorithm implementation and performance depend on several parameters, such as cognitive parameter, social parameter, and constriction coefficient. These parameters are tuned by using trial and error. To reduce the parametrization of a PSO method, this work presents two efficient hybrid SGO approaches, namely, a real-coded genetic algorithm-based PSO (RGA-PSO) method and an artificial immune algorithm-based PSO (AIA-PSO) method. The specific parameters of the internal PSO algorithm are optimized using the external RGA and AIA approaches, and then the internal PSO algorithm is applied to solve UGO problems. The performances of the proposed RGA-PSO and AIA-PSO algorithms are then evaluated using a set of benchmark UGO problems. Numerical results indicate that, besides their ability to converge to a global minimum for each test UGO problem, the proposed RGA-PSO and AIA-PSO algorithms outperform many hybrid SGO algorithms. Thus, the RGA-PSO and AIA-PSO approaches can be considered alternative SGO approaches for solving standard-dimensional UGO problems.

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Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches

Mathematical Problems in Engineering ◽

10.1155/2012/841410 ◽

2012 ◽

Vol 2012 ◽

pp. 1-36 ◽

Cited By ~ 8

Author(s):

Jui-Yu Wu

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Optimization Methods ◽

Pso Algorithm ◽

Global Optimum ◽

Constrained Global Optimization ◽

Generalized Polynomial ◽

Real Coded Genetic Algorithm ◽

Constriction Coefficient ◽

Optimum Solution

This work presents a hybrid real-coded genetic algorithm with a particle swarm optimization (RGA-PSO) algorithm and a hybrid artificial immune algorithm with a PSO (AIA-PSO) algorithm for solving 13 constrained global optimization (CGO) problems, including six nonlinear programming and seven generalized polynomial programming optimization problems. External RGA and AIA approaches are used to optimize the constriction coefficient, cognitive parameter, social parameter, penalty parameter, and mutation probability of an internal PSO algorithm. CGO problems are then solved using the internal PSO algorithm. The performances of the proposed RGA-PSO and AIA-PSO algorithms are evaluated using 13 CGO problems. Moreover, numerical results obtained using the proposed RGA-PSO and AIA-PSO algorithms are compared with those obtained using published individual GA and AIA approaches. Experimental results indicate that the proposed RGA-PSO and AIA-PSO algorithms converge to a global optimum solution to a CGO problem. Furthermore, the optimum parameter settings of the internal PSO algorithm can be obtained using the external RGA and AIA approaches. Also, the proposed RGA-PSO and AIA-PSO algorithms outperform some published individual GA and AIA approaches. Therefore, the proposed RGA-PSO and AIA-PSO algorithms are highly promising stochastic global optimization methods for solving CGO problems.

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Improved Particle Swarm Optimizer with Dynamically Adjusted Search Space and Velocity Limits for Global Optimization

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213015500177 ◽

2015 ◽

Vol 24 (05) ◽

pp. 1550017 ◽

Cited By ~ 1

Author(s):

Aderemi Oluyinka Adewumi ◽

Akugbe Martins Arasomwan

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Search Space ◽

Particle Swarm Optimizer ◽

Search Range ◽

Original Algorithm ◽

Inertia Weight ◽

And Performance

This paper presents an improved particle swarm optimization (PSO) technique for global optimization. Many variants of the technique have been proposed in literature. However, two major things characterize many of these variants namely, static search space and velocity limits, which bound their flexibilities in obtaining optimal solutions for many optimization problems. Furthermore, the problem of premature convergence persists in many variants despite the introduction of additional parameters such as inertia weight and extra computation ability. This paper proposes an improved PSO algorithm without inertia weight. The proposed algorithm dynamically adjusts the search space and velocity limits for the swarm in each iteration by picking the highest and lowest values among all the dimensions of the particles, calculates their absolute values and then uses the higher of the two values to define a new search range and velocity limits for next iteration. The efficiency and performance of the proposed algorithm was shown using popular benchmark global optimization problems with low and high dimensions. Results obtained demonstrate better convergence speed and precision, stability, robustness with better global search ability when compared with six recent variants of the original algorithm.

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Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm

Advances in Engineering Software ◽

10.1016/j.advengsoft.2018.11.004 ◽

2019 ◽

Vol 127 ◽

pp. 106-123 ◽

Cited By ~ 9

Author(s):

Ali Mortazavi ◽

Vedat Toğan ◽

Mahsa Moloodpoor

Keyword(s):

Swarm Intelligence ◽

Optimization Algorithm ◽

Optimization Problems ◽

Mathematical Optimization ◽

Hybrid Swarm ◽

Swarm Intelligence Optimization

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

PeerJ Computer Science ◽

10.7717/peerj-cs.696 ◽

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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A New PSO Algorithm with Crossover Operator for Global Optimization Problems

Advances in Soft Computing - Innovations in Hybrid Intelligent Systems ◽

10.1007/978-3-540-74972-1_29 ◽

2007 ◽

pp. 215-222 ◽

Cited By ~ 24

Author(s):

Millie Pant ◽

Radha Thangaraj ◽

Ajith Abraham

Keyword(s):

Global Optimization ◽

Optimization Problems ◽

Pso Algorithm ◽

Crossover Operator

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On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

The Scientific World JOURNAL ◽

10.1155/2013/860289 ◽

2013 ◽

Vol 2013 ◽

pp. 1-12 ◽

Cited By ~ 30

Author(s):

Martins Akugbe Arasomwan ◽

Aderemi Oluyinka Adewumi

Keyword(s):

Global Optimization ◽

Particle Swarm Optimization ◽

Optimization Problems ◽

Particle Swarm ◽

Pso Algorithm ◽

Search Space ◽

Terminal Point ◽

Simulation Experiments ◽

Swarm Optimization ◽

Inertia Weight

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

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A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

Knowledge-Based Systems ◽

10.1016/j.knosys.2018.05.002 ◽

2018 ◽

Vol 156 ◽

pp. 12-42 ◽

Cited By ~ 19

Author(s):

Diptangshu Pandit ◽

Li Zhang ◽

Samiran Chattopadhyay ◽

Chee Peng Lim ◽

Chengyu Liu

Keyword(s):

Global Optimization ◽

Swarm Intelligence ◽

Optimization Problems ◽

Intelligence Algorithm ◽

Swarm Intelligence Algorithm

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A New Compound Swarm Intelligence Algorithms for Solving Global Optimization Problems

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v10i9.1389 ◽

2013 ◽

Vol 10 (9) ◽

pp. 2010-2020

Author(s):

Ibrahim M. Hezam ◽

Osama Abdel Raouf ◽

Mohey M. Hadhoud

Keyword(s):

Swarm Intelligence ◽

Firefly Algorithm ◽

Research Study ◽

Optimization Problems ◽

Cuckoo Search ◽

Hybrid Swarm ◽

Swarm Optimization ◽

Intelligence Algorithm ◽

Swarm Algorithms ◽

Swarm Intelligence Algorithm

This paper proposes a new hybrid swarm intelligence algorithm that encompasses the feature of three major swarm algorithms. It combines the fast convergence of the Cuckoo Search (CS), the dynamic root change of the Firefly Algorithm (FA), and the continuous position update of the Particle Swarm Optimization (PSO). The Compound Swarm Intelligence Algorithm (CSIA) will be used to solve a set of standard benchmark functions. The research study compares the performance of CSIA with that of CS, FA, and PSO, using the same set of benchmark functions. The comparison aims to test if the performance of CSIA is Competitive to that of the CS, FA, and PSO algorithms denoting the solution results of the benchmark functions.

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