WO-2026091813-A1 - MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION METHOD AND SYSTEM BASED ON MULTI-STRATEGY IMPROVEMENT

Abstract

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

Inventors

• ZHANG, BIN
• JIANG, Yanyan
• ZHAO, Shanliang
• QUAN, Yu
• ZHAO, JINMING
• BAO, Yongsheng
• FANG, DONG
• HOU, Mingjian
• ZHANG, QI
• ZHANG, MING
• YU, RUI
• ZHAO, Huide
• LV, Yang

Assignees

• 华能牙克石发电有限公司

Dates

Publication Date

20260507

Application Date

20250825

Priority Date

20241030

Claims (10)

1. A multi-strategy improved multi-objective particle swarm optimization method, characterized in that the multi-strategy improved multi-objective particle swarm optimization method includes: Initialize the weight vector, reference point, particle velocity, and position, and use an adaptive nonlinear correction strategy to initialize the inertial weights; The individual optimal solution is updated using a decomposition strategy, the dynamic density distance of particles is calculated, the global optimal solution is selected by combining the roulette wheel algorithm, and a mutation factor is introduced to randomly mutate the particles, thereby increasing the diversity and randomness of the population. Update external archives, store Pareto optimal solution sets, remove inferior solutions, and filter out solutions that are closer to the Pareto front through Pareto dominance relations.
2. The multi-objective particle swarm optimization method based on multi-strategy improvement according to claim 1 is characterized in that the initialization of inertia weight includes: using an adaptive nonlinear correction strategy for inertia weight in the optimization algorithm, and adjusting the inertia weight from the maximum value to the minimum value nonlinearly by using an exponential function as the number of iterations increases, thereby enhancing the search performance of the algorithm by adjusting the adaptive control parameter alpha.
3. According to claim 2, the multi-strategy improved multi-objective particle swarm optimization method is characterized in that the decomposition strategy includes: the optimization model of the hybrid energy storage system is a bi-objective optimization, using the Tchebycheff aggregation function for scalarization calculation, and guiding the population evolution by pre-setting a set of uniformly distributed reference weight vectors in the objective space, with each individual in the population corresponding to a reference weight vector. During the initialization operation in the early stage of the algorithm, the decomposition algorithm is incorporated, and the multi-objective problem is scalarized and decomposed into n independent sub-problems using the Tchebycheff decomposition method, with each sub-problem corresponding to a specific weight vector.
4. The multi-objective particle swarm optimization method based on multi-strategy improvement according to claim 3 is characterized in that the Tchebycheff decomposition method includes: performing scalarization calculation using the Tchebycheff aggregation function; The formula is expressed as: Where gTCH represents the aggregation function, λi is the reference weight vector of the i-th particle, which is selected using the uniform distribution method, z * represents the reference point, R represents the decision space, and fi (x) represents each objective.
5. According to claim 4, the multi-strategy improved multi-objective particle swarm optimization method is characterized in that the individual optimal solution includes: evaluating the decision results using the Pareto optimality concept. Pareto optimality describes a state where, without compromising any objective, it is impossible to further enhance the advantage of a certain objective. In a decision space containing other solutions, if a solution is Pareto-optimal, meaning that the decision vector of the current solution is not dominated by any other vector, then this solution is called a Pareto optimal solution. When solving multi-objective optimization problems, the optimal solution consists of a series of Pareto optimal solutions that cannot dominate each other. The set of solutions is called the Pareto optimal solution set. Correspondingly, the corresponding point of the current solution in the objective function space forms the Pareto front.
6. The multi-objective particle swarm optimization method based on multi-strategy improvement according to claim 5 is characterized in that the selection of the global optimal solution includes: using a method combining dynamic density distance sorting and roulette wheel algorithm as the global optimal solution selection strategy, where dynamic density distance reflects the density between particles and the uniformity of the solution, and the density distance formula for particle x_i in the multi-objective optimization model of the hybrid energy storage system is: Where xj and xk represent the two particles closest to particle xi , f1,max represents the maximum value of the first objective function, and f2 ,max represents the maximum value of the second objective function; The particles in the Pareto optimal solution set are sorted by dense distance. From the top 20% of the solutions, a roulette wheel algorithm is used to select a solution as the global optimal solution, which guides the direction of population evolution.
7. The multi-objective particle swarm optimization method based on multi-strategy improvement according to claim 1 is characterized in that the updating of the external archive includes: due to the existence of the particle swarm, the particle swarm algorithm generates multiple non-dominant solutions in a single execution, and an external archive method is adopted, wherein the archive is intended to store the Pareto optimal solution set, prevent the archive from being over-expanded, increase computational complexity, and impose a limit on the maximum storage capacity of the external archive to ensure normal operation.
8. A system applying the multi-strategy improved multi-objective particle swarm optimization method according to any one of claims 1 to 7, characterized in that the multi-strategy improved multi-objective particle swarm optimization system includes: an initialization module, an optimization strategy execution module, a global optimal solution selection module, and an external file management module; The initialization module is responsible for initializing the particle's position, velocity, weight vector, and reference point parameters, and implementing an adaptive nonlinear correction strategy to initialize the inertial weights. The optimization strategy execution module applies the Tchebycheff decomposition method to scalarize the multi-objective problem, guides population evolution, updates the optimal solution of individuals, calculates the dynamic density distance of particles, and introduces a mutation factor to randomly mutate the particles. The global optimal solution selection module sorts the Pareto optimal solution set according to the dynamic dense distance and uses the roulette wheel algorithm to select the global optimal solution from the sorted solutions. The external file management module stores and manages the Pareto optimal solution set, filters better solutions through Pareto dominance relations, controls the size of the external file, and avoids over-expansion and computational complexity.
9. A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the multi-strategy improved multi-objective particle swarm optimization method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the multi-strategy improved multi-objective particle swarm optimization method according to any one of claims 1 to 7.

Description

A Multi-Strategy Improved Multi-Objective Particle Swarm Optimization Method and System This application claims priority to Chinese Patent Application No. 202411532710.9, filed on October 30, 2024, entitled "A Multi-Target Particle Swarm Optimization Method and System Based on Multi-Strategy Improvement", the entire contents of which are incorporated herein by reference. Technical Field This application relates to the field of power system optimization technology, and in particular to a multi-objective particle swarm optimization method and system based on multi-strategy improvement. Background Technology The generator condition monitoring device is battery powered, making it more flexible and reliable in generator maintenance sites. Currently, lithium-ion batteries are widely used due to their advantages such as high energy density, low self-discharge rate, and long cycle life. In some generator condition monitoring projects, the power supply used by the monitoring instruments is required to provide pulse power. A hybrid energy storage system employing both energy-type and power-type energy storage elements can optimize the overall performance of the generator condition monitoring device's own power supply system. Optimizing the power supply of a detection device based on a hybrid energy storage system is a complex nonlinear optimization problem with multiple objectives, constraints, and variables. It is difficult to solve using traditional mathematical methods. For example, the traditional particle swarm optimization method is prone to getting trapped in local optima, and the obtained solution set is diverse or has poor convergence when solving multi-objective optimization problems. Summary of the Invention The purpose of this application is to provide a multi-strategy improved multi-objective particle swarm optimization method and system, which can solve the problems of premature convergence to local non-dominated solutions and Pareto front suboptimal distribution caused by improper external archive update strategy in solving multi-objective optimization problems by traditional multi-objective particle swarm optimization algorithm, thereby improving the efficiency and reliability of the device power system. To solve the above-mentioned technical problems, this application provides the following technical solution: Firstly, this application provides a multi-strategy improved multi-objective particle swarm optimization method, which includes: initializing weight vectors, reference points, and particle velocities and positions; initializing inertial weights using an adaptive nonlinear correction strategy; updating individual optimal solutions using a decomposition strategy; calculating the dynamic density distance of particles; selecting the global optimal solution using a roulette wheel algorithm; introducing a mutation factor to randomly mutate particles, increasing the diversity and randomness of the population; updating external archives; storing the Pareto optimal solution set; eliminating inferior solutions; and selecting solutions closer to the Pareto front through Pareto dominance relations. Optionally, the initialization of inertia weights includes: optimizing the inertia weights in the algorithm using an adaptive nonlinear correction strategy for inertia weights; as the number of iterations increases, the use of an exponential function nonlinearly adjusts the inertia weights from a maximum value to a minimum value; and the search performance of the algorithm is enhanced by adjusting the adaptive control parameter alpha. Optionally, the decomposition strategy includes: the optimization model of the hybrid energy storage system is a bi-objective optimization, using the Tchebycheff aggregation function for scalarization calculation, and guiding the population evolution by pre-setting a set of uniformly distributed reference weight vectors in the objective space, with each individual in the population corresponding to a reference weight vector. During the initialization operation in the early stage of the algorithm, the decomposition algorithm is incorporated, and the multi-objective problem is scalarized and decomposed into n independent sub-problems using the Tchebycheff decomposition method, with each sub-problem corresponding to a specific weight vector. Optionally, the Tchebycheff decomposition method includes: performing scalarization calculations using the Tchebycheff aggregation function; The formula is expressed as: Where gTCH represents the aggregation function, λi is the reference weight vector of the i-th particle, which is selected using the uniform distribution method, z * represents the reference point, R represents the decision space, and fi (x) represents each objective. Optionally, the individual optimal solution includes: evaluating the decision result using the concept of Pareto optimality. Pareto optimality describes a state in which the advantage of a certain objective cannot be