Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

Basu, Mousumi

doi:10.1007/s40031-015-0204-6

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…The accuracy of the algorithm, the algorithm flow chart is shown in Figure 4: Table 1 shows the basic parameters of each test function. Single-peak [26] and multi-peak [27] are selected to evaluate the convergence speed of the algorithm [28] and the ability to jump out of the local optimum. In the formula, the F1 function and F2 function are single-peak functions, which are mainly used to test the convergence performance of the improved algorithm in the text; F3~F10 functions are all multi-peak functions, among which F8 is a complex multi-peak function, and the global minimum is located at In a very narrow valley, F9 is moving, rotating, inseparable, and expandable multimodal function.…”

Section: Antlion Optimization Algorithm Fused With Immune Cloning，(icalo)mentioning

confidence: 99%

Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

2021

Preprint

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Antlion optimization algorithm has good search and development capabilities, but the influence weight of elite ant lions is reduced in the later stage of optimization, which leads to slower algorithm convergence and easy to fall into local optimization. For this purpose, an antlion optimization algorithm based on immune cloning was proposed. In the early stage, the reverse learning strategy was used to initialize the ant population. The Cauchy mutation operator was added to the elite antlion update to improve the later development ability of the algorithm; finally, the antlion was cloned and mutated with the immune clone selection algorithm to change the position and fitness value of the antlion, and further improve the algorithm's global optimization ability and convergence accuracy. 10 test functions and a 0~1 backpack were used to evaluate the optimization ability of the algorithm and applied to the size and layout optimization problems of the truss structure. The optimization effect was found to be good through the force effect diagram. It is verified that ICALO is applied to combinatorial optimization problems with faster convergence speed and higher accuracy. It provides a new method for structural optimization.This article is submitted as original content. The authors declare that they have no competing interests.

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Section: Antlion Optimization Algorithm Fused With Immune Cloning，(icalo)mentioning

confidence: 99%

Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

2021

Preprint

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“…Vishnu et al [23] applied an Enhanced Particle Swarm Optimization to solving the problem. Basu et al [24] applied Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions. Arya et al [25] In this paper Accipitridae Optimization Algorithm (AOA) is applied to solve the Factual power loss lessening problem.…”

Section: Introductionmentioning

confidence: 99%

Real Power Loss Reduction by Accipitridae Optimization Algorithm

Lenin

2022

Technol Econ Smart Grids Sustain Energy

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In this paper, Accipitridae Optimization Algorithm (AOA) is used to solve the power loss lessening problem. Authorizing a satisfactory magnitude of reactive power is effervescent for the steadfast operation of power transmission structures, as the power meagreness may tip to staid power catastrophe and leading power upheavals. Loss reduction problem is intended as one of the prominent conditions for compassionate and financial action of edifice. It is exceptional by appropriate union of the network apparatus castoff to deal with up the power flow with the dispassionate of decreasing the losses and evolution the power outline of the assemblage. Plentiful previous studies unravelled the power problem as the optimization objective tasks premeditated noticeably. Real power loss reduction, Voltage stability expansion and voltage deviation minimization are the key objectives of the work. Hunting methods of Accipitridae are imitated to design the Accipitridae Optimization Algorithm (AOA). Accipitridae does hunting in a single-mode on the prey. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae. High ascend with a perpendicular bend down towards the prey will be done by the Accipitridae is formulated. As soon as the prey spot is precise then Accipitridae is geared up for landing and assault, the Accipitridae move down perpendicularly with a preface assault to determine the prey response. AOA initiates the upgrading measures by engendering an arbitrary solution. In the course of the curve of reiteration, the explore stratagem of the AOA is the rational location of the most excellent optimal solution. The authenticity of the Accipitridae Optimization Algorithm (AOA) is corroborated in IEEE 30 bus system (with and devoid of L-index). Genuine power loss lessening is attained. Voltage stability escalated with minimization of power deviance. Then the proportion of actual power loss lessening is amplified.

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“…Proposed in 1995, PSO was applied to optimization, biomedicine, communication, control, plan, prediction, filter, and parameter estimation in rainfall-runoff modeling and so forth [24][25][26][27][28][29][30]. It was improved in selecting the parameter, the velocity equation of the particle, uncertainty stimulation, learning abilities, stability, convergence, and more [31][32][33][34][35][36][37][38][39][40][41]. Wang et al and Chen et al applied PSO to solve optimization of a nine-work network plan [42,43].…”

Section: Introductionmentioning

confidence: 99%

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

Zhang

Yang

2017

Mathematical Problems in Engineering

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No relevant reports have been reported on the optimization of a large-scale network plan with more than 200 works due to the complexity of the problem and the huge amount of computation. In this paper, an improved particle swarm optimization algorithm via optimization of initial particle swarm (OIPSO) is first explained by the stochastic processes theory. Then two optimization examples are solved using this method which are the optimization of resource-leveling with fixed duration and the optimization of resources constraints with shortest project duration in a large network plan with 223 works. Through these two examples, under the same number of iterations, it is proven that the improved algorithm (OIPSO) can accelerate the optimization speed and improve the optimization effect of particle swarm optimization (PSO).

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Improved Particle Swarm Optimization for Global Optimization of Unimodal and Multimodal Functions

Cited by 7 publications

References 32 publications

Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

Antlion Algorithm Based on Immune Cloning and Its Truss Optimization

Real Power Loss Reduction by Accipitridae Optimization Algorithm

Large‐Scale Network Plan Optimization Using Improved Particle Swarm Optimization Algorithm

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