Multifactorial Particle Swarm Optimization Enhanced by Hybridization With Firefly Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 172-187
Author(s):  
Heng Xiao ◽  
Yokoya ◽  
Toshiharu Hatanaka
Keyword(s):  
Numerical Experiments ◽  
Firefly Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Selection Mechanism ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Search Capability ◽  
Evolutionary Multitasking ◽  
Optimization Solvers

In recent years, evolutionary multitasking has received attention in the evolutionary computation community. As an evolutionary multifactorial optimization method, multifactorial evolutionary algorithm (MFEA) is proposed to realize evolutionary multitasking. One concept called the skill factor is introduced to assign a preferred task for each individual in MFEA. Then, based on the skill factor, there are some multifactorial optimization solvers including swarm intelligence that have been developed. In this paper, a PSO-FA hybrid model with a model selection mechanism triggered by updating the personal best memory is applied to multifactorial optimization. The skill factor reassignment is introduced in this model to enhance the search capability of the hybrid swarm model. Then numerical experiments are carried out by using nine benchmark problems based on typical multitask situations and by comparing with a simple multifactorial PSO to show the effectiveness 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.

Heuristic Optimization Algorithms for Power System Scheduling Applications

2020 ◽  
pp. 178-205
Author(s):  
Anongpun Man-Im ◽  
Weerakorn Ongsakul ◽  
Nimal Madhu M.
Keyword(s):  
Power System ◽  
Power Loss ◽  
Optimization Method ◽  
Heuristic Optimization ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Trade Off ◽  
Fuzzy Function ◽  
Power System Scheduling ◽  
Search Capability

Power system scheduling is one of the most complex multi-objective scheduling problems, and a heuristic optimization method is designed for finding the OPF solution. Stochastic weight trade-off chaotic mutation-based non-dominated sorting particle swarm optimization algorithm can improve solution-search-capability by balancing between global best exploration and local best utilization through the stochastic weight and dynamic coefficient trade-off methods. This algorithm with chaotic mutation enhances diversity and search-capability, preventing premature convergence. Non-dominated sorting and crowding distance techniques efficiently provide the optimal Pareto front. Fuzzy function is used to select the local best compromise. Using a two-stage approach, the global best solution is selected from many local trials. The discussed approach can schedule the generators in the systems effectively, leading to savings in fuel cost, reduction in active power loss and betterment in voltage stability.

scholarly journals Improved Dynamical Particle Swarm Optimization Method for Structural Dynamics

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
W. D. Annicchiarico ◽  
M. Cerrolaza
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Nonlinear Problems ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Structural Behaviour ◽  
Seismic Excitations ◽  
Swarm Optimization ◽  
Building Model ◽  
Optimization Methodology

A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.

scholarly journals A New Compound Swarm Intelligence Algorithms for Solving Global Optimization Problems

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.

scholarly journals Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1539
Author(s):  
Joonwoo Lee ◽  
Won Kim
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Particle Swarm ◽  
Optimization Method ◽  
High Dimensional ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Optima ◽  
One Step ◽  
Bare Bones

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.

A HYBRID OPTIMIZATION METHOD BASED ON DIFFERENTIAL EVOLUTION AND HARMONY SEARCH

2014 ◽  
Vol 13 (01) ◽  
pp. 1450001 ◽  
Author(s):  
X. Z. GAO ◽  
X. WANG ◽  
S. J. OVASKA ◽  
K. ZENGER
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Convergence Property ◽  
Harmony Search ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Benchmark Problems ◽  
Nature Inspired Computing ◽  
Search Capability

The differential evolution (DE) and harmony search (HS) are two well-known nature-inspired computing techniques. Both of them can be applied to effectively cope with nonlinear optimization problems. In this paper, we propose and study a new DE method, DE–HS, by utilizing the fresh individual generation mechanism of the HS. The HS-based approach can enhance the local search capability of the original DE. Optimization of some unconstrained and constrained benchmark problems and a real-world wind generator demonstrate that our DE–HS has an improved convergence property.

Stochastic Methods for Hard Optimization

2010 ◽  
pp. 182-220
Author(s):  
Rosario Toscano
Keyword(s):  
Numerical Experiments ◽  
Optimization Problems ◽  
Optimization Method ◽  
Point Of View ◽  
Stochastic Methods ◽  
Robust Controller ◽  
Swarm Optimization ◽  
Residual Generator ◽  
Fixed Structure ◽  
Interesting Alternative

This chapter aims at solving difficult optimization problems arising in many engineering areas. To this end, a brief review of the main stochastic methods which can be used for solving continuous non-convex constrained optimization problems is presented i.e.: Simulated annealing (SA), Genetic algorithm (GA), and Particle swarm optimization (PSO). In addition to that, we will present a recently developed optimization method called Heuristic Kalman Algorithm (HKA) which seems to be, in some cases, an interesting alternative to the conventional approaches. The performance of these methods depends dramatically on the feasible search domain used to find out a solution as well as the initialization of the various user defined parameters. From this point of view, some practical indications concerning these issues will be given. Another objective of this chapter is to show that the stochastic methods, notably HKA, can be efficiently used to solve robust synthesis problems in the area of structured control and fault diagnosis systems. More precisely, we will deal with the following problems: the synthesis of a robust controller with a given fixed structure and the design of a robust residual generator. Some numerical experiments exemplify the resolution of this kind of problems.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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