Multi-Objective Particle Swarm Optimization Based on Fuzzy Optimality

Abstract

In order to overcome the limitations of the Pareto optimality in solving multi-objective optimization problems, a new optimality definition, fuzzy optimality is proposed, which considered both of the numbers of improved objectives and the extent of the improvements. Then, the fuzzy optimalitybased multi-objective particle swarm optimization algorithm is presented. It inherits the basic structure of the particle swarm optimization and evaluates the particles by the fuzzy optimality. The numerical experiments are carried out on 6 representative test functions, and the results show that the proposed fuzzy optimality based multi-objective particle swarm optimization algorithm shows better performance on aspects of quality of solutions, robustness, and computational complexity, compared with the results of the NSGA-II and MOPSO. Finally, the efficacy and practicality of the proposed approach are validated in the APU fuel consumption and emissions multi-objective optimization problem.

Electricity optimal scheduling strategy considering multiple parks shared energy in the absence of grid power supply

“…The parameters of MOPSO in simulation such as inertia weights, acceleration coefficient 1, and acceleration coefficient 2 are set to 0.4, 2.0 and 2.0, respectively. This section mainly refers to the literature 21,31 to set the algorithm parameters.…”

“…The Pareto solution sets of customer interruption costs, cost of BESS and customer dissatisfaction obtained by simulation under different algorithms are shown in Figure 6A. According to the three objective model formulas (23), (24), (31) and the constraints and function parameters, a scatterplot of the objective function can be drawn, which represents all the solutions that the objective function can obtain under the constraints, as shown of the square in Figure 6A. And the fitness of all solutions is calculated according to the fitness function.…”

“…It can be seen that α shortage = 33%. From formulas (23) and (31) and the parameters in Tables 1 and 3, the cost of all flexible load interruptions and the customer dissatisfaction caused by all flexible load interruptions in the multi-park area can be calculated as 1.3 × 10 5 ¥ and 12. So, the LBCIC is 4.29 × 10 4 ¥.…”

Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

“…Also, it uses a fuzzy system to assign the correct application rate to these four operators. Shen et al (Shen et al 2019) proposed a multi-objective particle swarm optimization algorithm based on fuzzy optimization, and the experiment has better performance in terms of solution quality, robustness and computational complexity. HSMP (Zou et al 2020) used the current and past continuous PS centers to automatically establish a T-S fuzzy nonlinear regression prediction model that can predict future PS centers to improve the prediction accuracy when environmental changes occur at the inflection point.…”

A Novel Service Composition Algorithm for Cloud-Based Manufacturing Environment

“…Hence, how to quickly select the optimal service composition to fulfil the demands has become a key issue in CMSC. For CMSC, scholars have carried out an enormous amount of research on it and proposed a variety of methods such as genetic algorithm (GA) [6], particle swarm optimization algorithm (PSO) [7], ant colony optimization algorithm (ACO) [8], artificial bee colony algorithm (ABC) [9], differential evolution algorithm (DE) [10] and gravitational search algorithm(GSA) [11].…”