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LADPSO: using fuzzy logic to conduct PSO algorithm

APPLIED INTELLIGENCE 2012年第2期37卷 290-304页

作者： Norouzzadeh, Mohammad Sadegh Ahmadzadeh, Mohammad Reza Palhang, Maziar Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran

optimization plays a critical role in human modern life. Nowadays, optimization is used in many aspects of human modern life including engineering, medicine, agriculture and economy. Due to the growing number of optimization problems and their growing complexity, we need to improve and develop theoretical and practical optimization methods. Stochastic population based optimization algorithms like genetic algorithms and particle swarm optimization are good candidates for solving complex problems efficiently. Particle swarm optimization (PSO) is an optimization algorithm that has received much attention in recent years. PSO is a simple and computationally inexpensive algorithm inspired by the social behavior of bird flocks and fish schools. However, PSO suffers from premature convergence, especially in high dimensional multi-modal functions. In this paper, a new method for improving PSO has been introduced. The Proposed method which has been named Light Adaptive Particle Swarm optimization is a novel method that uses a fuzzy control system to conduct the standard algorithm. The suggested method uses two adjunct operators along with the fuzzy system in order to improve the base algorithm on global optimization problems. Our approach is validated using a number of common complex uni-modal/multi-modal benchmark functions and results have been compared with the results of Standard PSO (SPSO2011) and some other methods. The simulation results demonstrate that results of the proposed approach is promising for improving the standard PSO algorithm on global optimization problems and also improving performance of the algorithm.

关键词： Particle swarm optimization Fuzzy control Random search numerical function optimization Premature convergence

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A PSO based approach: Scout particle swarm algorithm for continuous global optimization problems

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2019年第2期6卷 129-142页

作者： Koyuncu, Hasan Ceylan, Rahime Konya Tech Univ Elect & Elect Engn Dept Konya Turkey

In the literature, most studies focus on designing new methods inspired by biological processes, however hybridization of methods and hybridization way should be examined carefully to generate more suitable optimization methods. In this study, we handle Particle Swarm optimization (PSO) and an efficient operator of Artificial Bee Colony optimization (ABC) to design an efficient technique for continuous function optimization. In PSO, velocity and position concepts guide particles to achieve convergence. At this point, variable and stable parameters are ineffective for regenerating awkward particles that cannot improve their personal best position (P-best). Thus, the need for external intervention is inevitable once a useful particle becomes an awkward one. In ABC, the scout bee phase acts as external intervention by sustaining the resurgence of incapable individuals. With the addition of a scout bee phase to standard PSO, Scout Particle Swarm optimization (ScPSO) is formed which eliminates the most important handicap of PSO. Consequently, a robust optimization algorithm is obtained. ScPSO is tested on constrained optimization problems and optimum parameter values are obtained for the general use of ScPSO. To evaluate the performance, ScPSO is compared with Genetic Algorithm (GA), with variants of the PSO and ABC methods, and with hybrid approaches based on PSO and ABC algorithms on numerical function optimization. As seen in the results, ScPSO results in better optimal solutions than other approaches. In addition, its convergence is superior to a basic optimization method, to the variants of PSO and ABC algorithms, and to the hybrid approaches on different numerical benchmark functions. According to the results, the Total Statistical Success (TSS) value of ScPSO ranks first (5) in comparison with PSO variants;the second best TSS (2) belongs to CLPSO and SP-PSO techniques. In a comparison with ABC variants, the best TSS value (6) is obtained by ScPSO, while TSS of Bit

关键词： Scout particle swarm optimization numerical function optimization Particle swarm optimization Artificial bee colony optimization Hybrid approach

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Modified Artificial Bee Colony Algorithm with Self-Adaptive Extended Memory

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CYBERNETICS AND SYSTEMS 2016年第7期47卷 585-601页

作者： Mao, Mingxuan Duan, Qichang Chongqing Univ Automat Coll Chongqing 400044 Peoples R China

To solve the problem of the poor solution precision and convergence speed in the artificial bee colony (ABC) algorithm, in this article we propose a modified algorithm called ABC algorithm with self-adaptive extended memory (ABCSEM) algorithm. First, the extended memory is introduced to store employed bees' historical information comprising recent food sources, personal best food sources, and global best food sources. Furthermore, the extended memory is added to the solution search equation to improve the exploitation capability. Experimental results conducted on a set of numerical benchmark functions show that the ABCSEM algorithm can outperform the ABC algorithm in most of the tested functions.

关键词： Artificial bee colony (ABC) algorithm exploitation capability numerical function optimization self-adaptive extended memory (SEM) solution search equation

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Two modified versions of artificial bee colony algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2013年 225卷 601-609页

作者： Alizadegan, Amir Asady, Babak Ahmadpour, Mohammad Amirkabir Univ Technol Dept Comp Engn & Informat Technol Tehran Iran Islamic Azad Univ Arak Branch Dept Comp Engn Arak Iran Islamic Azad Univ Maku Branch Dept Math Maku Iran

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms used in optimization problems. ABC simulates the intelligent foraging behavior of a honeybee swarm. In this paper, two aspects of ABC algorithm are modified and new configurations are used. The modified versions are tested on some well-known benchmark functions. Results show that the new changes have positive effects on the performance of ABC algorithm. (C) 2013 Published by Elsevier Inc.

关键词： Swarm intelligence optimization algorithms Artificial bee colony algorithm numerical function optimization Unconstrained optimization

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A new metaheuristic for optimization: Optics inspired optimization (OIO)

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COMPUTERS & OPERATIONS RESEARCH 2015年第Mar.期55卷 99-125页

作者： Kashan, Ali Husseinzadeh Tarbiat Modares Univ Fac Engn Dept Ind Engn Tehran Iran

Due to the law of reflection, a concave reflecting surface/mirror causes the incident light rays to converge and a convex surface/mirror causes the light rays to reflect away so that they all appear to be diverging. These converging and diverging behaviors cause that the curved mirrors show different image types depending on the distance between the object and the mirror. We model such optical phenomena metaphorically into the searching process of numerical optimization by a new algorithm called optics inspired optimization (OIO). OIO treats the surface of the numerical function to be optimized as a reflecting surface in which each peak is assumed to reflect as a convex mirror and each valley to reflect as a concave one. Each individual is assumed to be an artificial object (or light point) that its artificially glittered ray is reflected back by the function surface, given that the surface is convex or concave, and the artificial image is formed (a candidate solution is generated within the search domain) based on the mirror equations adopted from physics of optics. Besides OIO, we introduce different variants of it, called ROIO (Rotation based OIO), and COIO (Convex combination based OIO) algorithms and conduct an extensive computational effort to find out the merit of the new algorithms. Our comparisons on benchmark test functions and a real world engineering design application (i.e., optimization of a centrifuge pump) demonstrate that the new algorithms are efficient and compete better than or similar to most of state of the art optimization algorithms with the advantage of accepting few input parameters. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： numerical function optimization Metaheuristics Optics Convex/concave mirrors

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A novel differential evolution algorithm for binary optimization

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COMPUTATIONAL optimization AND APPLICATIONS 2013年第2期55卷 481-513页

作者： Kashan, Mina Husseinzadeh Kashan, Ali Husseinzadeh Nahavandi, Nasim Tarbiat Modares Univ Fac Engn Tehran Iran Islamic Azad Univ Fac Engn Sci & Res Branch Dept Syst & Ind Engn Tehran Iran

Differential evolution (DE) is one of the most powerful stochastic search methods which was introduced originally for continuous optimization. In this sense, it is of low efficiency in dealing with discrete problems. In this paper we try to cover this deficiency through introducing a new version of DE algorithm, particularly designed for binary optimization. It is well-known that in its original form, DE maintains a differential mutation, a crossover and a selection operator for optimizing non-linear continuous functions. Therefore, developing the new binary version of DE algorithm, calls for introducing operators having the major characteristics of the original ones and being respondent to the structure of binary optimization problems. Using a measure of dissimilarity between binary vectors, we propose a differential mutation operator that works in continuous space while its consequence is used in the construction of the complete solution in binary space. This approach essentially enables us to utilize the structural knowledge of the problem through heuristic procedures, during the construction of the new solution. To verify effectiveness of our approach, we choose the uncapacitated facility location problem (UFLP)-one of the most frequently encountered binary optimization problems-and solve benchmark suites collected from OR-Library. Extensive computational experiments are carried out to find out the behavior of our algorithm under various setting of the control parameters and also to measure how well it competes with other state of the art binary optimization algorithms. Beside UFLP, we also investigate the suitably of our approach for optimizing numerical functions. We select a number of well-known functions on which we compare the performance of our approach with different binary optimization algorithms. Results testify that our approach is very efficient and can be regarded as a promising method for solving wide class of binary optimization problems.

关键词： Binary optimization Uncapacitated facility location problem numerical function optimization Differential evolution Dissimilarity measure of binary vectors

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Particle state change algorithm

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SOFT COMPUTING 2018年第8期22卷 2641-2666页

作者： Feng, Xiang Xu, Hanyu Yu, Huiqun Luo, Fei East Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Smart City Collaborat Innovat Ctr Shanghai Peoples R China

The matter state change, which is a common phenomenon in nature, shows the process that the matter how to reach the optimal state in the environment. This paper presents a novel particle state change model (PSCM) inspired by mimicking the process of the matter state change. Based on PSCM, a novel particle state change (PSC) algorithm is proposed for solving continuous optimization problems. As a new algorithm, PSC has many differences from other similar nature-inspired algorithms in terms of the basic principle models, mathematical formalization and properties. This paper considers three states of the matter, namely gas state, liquid state and solid state. In a certain circumstance, the matter always converts from an unstable state into a stable state. It is similar to find the optimal solution of an optimization problem. The proposed algorithm also has the advantages in the respects of higher intelligence, effectiveness and lower computation complexity. And the convergence property of PSC is discussed in detail. In order to illustrate the ability of solving optimization problems in continuous domain, the new proposed algorithm is tested on basic function optimization, CEC2016 single-objective real-parameter numerical optimization and CEC2016 learning-base real-parameter single-objective optimization, and compared with eleven existing algorithms. The numerous simulations have shown the effectiveness and suitability of the proposed approach.

关键词： Particle state change algorithm Nature-inspired algorithm Matter state change numerical function optimization

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Hybrid Power Bank Deployment Model for Energy Supply Coverage optimization in Industrial Wireless Sensor Network

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Intelligent Automation & Soft Computing 2023年第8期37卷 1531-1551页

作者： Hang Yang Xunbo Li Witold Pedrycz School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6G 1H9Canada

Energy supply is one of the most critical challenges of wireless sensor networks(WSNs)and industrial wireless sensor networks(IWSNs).While research on coverage optimization problem(COP)centers on the network’s monitoring coverage,this research focuses on the power banks’energy supply *** study of 2-D and 3-D spaces is typical in IWSN,with the realistic environment being more complex with obstacles(i.e.,machines).A 3-D surface is the field of interest(FOI)in this work with the established hybrid power bank deployment model for the energy supply COP optimization of *** hybrid power bank deployment model is highly adaptive and flexible for new or existing plants already using the IWSN *** model improves the power supply to a more considerable extent with the least number of power bank *** main innovation in this work is the utilization of a more practical surface model with obstacles and training while improving the convergence speed and quality of the heuristic *** overall probabilistic coverage rate analysis of every point on the FOI is provided,not limiting the scope to target points or ***’s algorithm is extended from 2-D to 3-D surface to enhance the probabilistic covering model for coverage measurement.A dynamic search strategy(DSS)is proposed to modify the artificial bee colony(ABC)and balance the exploration and exploitation ability for better convergence toward eliminating NP-hard deployment ***,the cellular automata(CA)is utilized to enhance the convergence *** case study based on two typical FOI in the IWSN shows that the CA scheme effectively speeds up the optimization *** experiments are conducted on four benchmark functions to validate the effectiveness of the proposed *** experimental results show that the proposed algorithm outperforms the ABC and gbest-guided ABC(GABC)*** results show that the proposed energy coverage optimization method based on

关键词： Industrial wireless sensor network hybrid power bank deployment model:energy supply coverage optimization artificial bee colony algorithm radio frequency numerical function optimization

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A Multilevel Cooperative Multi-Population Cultural Algorithm

A Multilevel Cooperative Multi-Population Cultural Algorithm

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IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications (INISTA)

作者： Singh, Dilpreet Zadeh, Pooya Moradian Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON Canada

ISBN: (纸本)9781538651506

A new architecture for Multi-Population Cultural Algorithm is proposed which incorporates a new Multilevel Selection framework (ML-MPCA). The approach used in this paper is based on biological group selection theory which aims to improve the capability of MPCA to tackle evolution of cooperation. A two-level selection process is introduced namely within-group selection and between-group selection. Individuals interact with the other members of the group in an evolutionary game that determines their fitness. If the group reaches a certain size, it splits into two daughter groups. We test our algorithm on CEC 2015 expensive benchmark functions to evaluate its performance. We show that our proposed algorithm improves solution accuracy and consistency. The model can be extended to more than two levels of selection and can also include migration.

关键词： Multilevel/Group Selection Evolutionary Computation Evolution of Cooperation numerical function optimization

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A Novel Artificial Bee Colony Algorithm 6

A Novel Artificial Bee Colony Algorithm

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Yi, Yujiang He, Renjie Natl Univ Def Technol Coll Informat Syst & Management Changsha Hunan Peoples R China

ISBN: (纸本)9781479949557

Artificial bee colony algorithm is a new population-based evolutionary method based on the intelligent behavior of honey bee swarm. It has shown more effective than other biological-inspired algorithms. However, there are still insufficiencies in ABC algorithm, which is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. For these insufficiencies, we propose a novel artificial bee colony algorithm (NABC) for numerical optimization problems in this paper to improve the exploitation capability by incorporating the current best solution into the search procedure. Experiments are conducted on a set of unimodal/multimodal benchmark functions. The experiments results of NABC have been compared with Gbest-guided artificial bee colony algorithm (G-ABC), improved artificial bee colony algorithm (I-ABC), Elitist artificial bee colony algorithm (E-ABC). The results show that NABC is superior to those algorithms in most of the tested functions.

关键词： swarm intelligence artificial bee colony algorithm numerical function optimization

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