• International Journal of Control, Automation, and Systems
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• 제6권4호
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• pp.559-570
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• 2008

This paper proposes an improved particle swarm optimization named PSO with Controllable Random Exploration Velocity (PSO-CREV) behaving an additional exploration behavior. Different from other improvements on PSO, the updating principle of PSO-CREV is constructed in terms of stochastic approximation diagram. Hence a stochastic velocity independent on cognitive and social components of PSO can be added to the updating principle, so that particles have strong exploration ability than those of conventional PSO. The conditions and main behaviors of PSO-CREV are described. Two properties in terms of "divergence before convergence" and "controllable exploration behavior" are presented, which promote the performance of PSO-CREV. An experimental method based on a complex test function is proposed by which the proper parameters of PSO-CREV used in practice are figured out, which guarantees the high exploration ability, as well as the convergence rate is concerned. The benchmarks and applications on FCRNN training verify the improvements brought by PSO-CREV.

• Structural Engineering and Mechanics
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• 제70권5호
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• pp.513-524
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• 2019

This study proposed an improved particle swarm optimization (IPSO) method ensemble with kriging model for model updating. By introducing genetic algorithm (GA) and grouping strategy together with elite selection into standard particle optimization (PSO), the IPSO is obtained. Kriging metamodel serves for predicting the structural responses to avoid complex computation via finite element model. The combination of IPSO and kriging model shall provide more accurate searching results and obtain global optimal solution for model updating compared with the PSO, Simulate Annealing PSO (SimuAPSO), BreedPSO and PSOGA. A plane truss structure and ASCE Benchmark frame structure are adopted to verify the proposed approach. The results indicated that the hybrid of kriging model and IPSO could serve for model updating effectively and efficiently. The updating results further illustrated that IPSO can provide superior convergent solutions compared with PSO, SimuAPSO, BreedPSO and PSOGA.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.850-851
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• 2007

This paper presents an efficient approach for solving the economic dispatch (ED) problems with prohibited operating zones using an improved particle swarm optimization (PSO). Although the PSO-based approaches have several advantages suitable to the heavily constrained nonconvex optimization problems, they still have the drawbacks such as local optimal trapping due to the premature convergence (i.e., exploration problem) and insufficient capability to find nearly-by extreme points (i.e., exploitation problem). This paper proposes an improved PSO framework adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. The proposed method is applied to ED problem with prohibited operating zones. Also, the results are compared with those of the state-of-the-art methods.

• 정보처리학회논문지B
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• 제18B권1호
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• pp.39-44
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• 2011

본 논문에서는 최근 Xin-She Yang에 의해 소개된 반딧불이 알고리즘(FA)에 휴리스틱을 적용하여 개선하는 방안을 제안한다. 또한 이를 위하여 기존의 FA를 이와 유사한 문제영역의 알고리즘인 Particle Swarm Optimization(PSO)와 정확도 측면, 수렴 시간 측면, 각 입자의 움직임 측면에서 비교 분석한다. 비교 실험 결과, FA의 정확도는 PSO보다 나쁘지 않았지만, 수렴 속도는 느린 것으로 나타났다. 본 논문은 이에 대한 직관적인 원인을 고찰하고, 이를 극복하기 위해, 기존의 FA에 부분 돌연변이 휴리스틱을 적용하여 개선된 FA(Improved FA)를 제안한다. 벤치마크 함수들을 최적화 하는 비교 실험 결과, 개선된 FA가 PSO와 기존의 FA보다 정확도와 수렴속도 측면에서 우수함을 보이고자 한다.

• 전기학회논문지
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• 제56권6호
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• pp.1023-1030
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• 2007

This paper presents a new and efficient approach for solving the economic dispatch (ED) problems with nonconvex cost functions using particle swarm optimization (PSO). Although the PSO is easy to implement and has been empirically shown to perform well on many optimization problems, it may easily get trapped in a local optimum when solving problems with multiple local optima and heavily constrained. This paper proposes an improved PSO, which combines the conventional PSO with chaotic sequences (CPSO). The chaotic sequences combined with the linearly decreasing inertia weights in PSO are devised to improve the global searching capability and escaping from local minimum. To verify the feasibility of the proposed method, numerical studies have been performed for two different nonconvex ED test systems and its results are compared with those of previous works. The proposed CPSO algorithm outperforms other state-of-the-art algorithms in solving ED problems, which consider valve-point and multi-fuels with valve-point effects.

• 지식경영연구
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• 제10권4호
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• pp.17-30
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• 2009

This study is concerned with suggesting a new clustering algorithm to evaluate the value of papers which were supported by research grants by Korea Research Fund (KRF). The algorithm is based on an extended version of a conventional PSO (Particle Swarm Optimization) mechanism. In other words, the proposed algorithm is based on integration of k-means algorithm and simulated annealing mechanism, named KASA-PSO. To evaluate the robustness of KASA-PSO, its clustering results are evaluated by research grants experts working at KRF. Empirical results revealed that the proposed KASA-PSO clustering method shows improved results than conventional clustering method.

• 한국정보전자통신기술학회논문지
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• 제16권4호
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• pp.203-209
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• 2023

Harmony search(HS)는 새로운 하모니를 구성할 때 HM을 참조하는 경우 개별 하모니의 평가를 이용하지 않지만 PSO(particle swarm optimization)는 개별 입자의 평가와 모집단의 평가를 이용하여 해를 찾아간다. 그러나 본 연구에서는 HS와 PSO의 유사점을 찾아 PSO의 입자 개선 과정을 HS에 적용하여 알고리즘의 성능을 향상시키고자 하였다. PSO 알고리즘을 적용하기 위해서는 개별 입자의 local best와 떼(swam)의 global best가 필요하다. 본 연구에서는 HS가 harmony memory(HM)에서 가장 나쁜 하모니을 개선하는 과정을 PSO와 매우 유사한 과정으로 보았다. 이에 따라 HM의 가장 나쁜 하모니를 입자의 PSO의 local best로, 가장 좋은 하모니는 PSO의 global best 최고로 간주하였다. 이와 같이 PSO의 입자 개선과정을 HS 하모니 개선과정에 도입하여 HS의 성능을 향상시킬 수 있었다. 본 연구의 결과는 다양한 함수에 대한 최적화 예시를 통해 비교 확인하였다. 그 결과 정확성과 일관성에 있어 기존 HS보다 제안한 HS-PSO가 매우 우수함을 알 수 있었다.

• Structural Engineering and Mechanics
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• 제50권3호
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• pp.323-347
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• 2014

This paper proposes a Particle Swarm Optimization (PSO) algorithm, which is improved by making use of the Harmony Search (HS) approach and called HS-PSO algorithm. A computer code is developed for optimal sizing design of non-linear steel frames with various semi-rigid and rigid beam-to-column connections based on the HS-PSO algorithm. The developed code selects suitable sections for beams and columns, from a standard set of steel sections such as American Institute of Steel Construction (AISC) wide-flange W-shapes, such that the minimum total cost, which comprises total member plus connection costs, is obtained. Stress and displacement constraints of AISC-LRFD code together with the size constraints are imposed on the frame in the optimal design procedure. The nonlinear moment-rotation behavior of connections is modeled using the Frye-Morris polynomial model. Moreover, the P-${\Delta}$ effects of beam-column members are taken into account in the non-linear structural analysis. Three benchmark design examples with several types of connections are presented and the results are compared with those of standard PSO and of other researches as well. The comparison shows that the proposed HS-PSO algorithm performs better both than the PSO and the Big Bang-Big Crunch (BB-BC) methods.

• Smart Structures and Systems
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• 제25권4호
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• pp.487-499
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• 2020

This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.

• Computers and Concrete
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• 제21권5호
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• pp.505-511
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• 2018

This paper investigates the potential of a hybrid model which combines the least squares support vector machine (LSSVM) and an improved particle swarm optimization (IMPSO) techniques for prediction of concrete compressive strength. A modified PSO algorithm is employed in determining the optimal values of LSSVM parameters to improve the forecasting accuracy. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed IMPSO-LSSVM model. Further, predictions from five models (the IMPSO-LSSVM, PSO-LSSVM, genetic algorithm (GA) based LSSVM, back propagation (BP) neural network, and a statistical model) were compared with the experimental data. The results show that the proposed IMPSO-LSSVM model is a feasible and efficient tool for predicting the concrete compressive strength with high accuracy.