DPb-MOPSO: A Dynamic Pareto bi-level Multi-objective Particle Swarm Optimization Algorithm

Maximum Lyapunov exponent-based multiple chaotic slime mold algorithm for real-world optimization

Slime mold algorithm (SMA) is a nature-inspired algorithm that simulates the biological optimization mechanisms and has achieved great results in various complex stochastic optimization problems. Owing to the simulated biological search principle of slime mold, SMA has a unique advantage in global optimization problem. However, it still suffers from issues of missing the optimal solution or collapsing to local optimum when facing complicated problems. To conquer these drawbacks, we consider adding a novel multi-chaotic local operator to the bio-shock feedback mechanism of SMA to compensate for the lack of exploration of the local solution space with the help of the perturbation nature of the chaotic operator. Based on this, we propose an improved algorithm, namely MCSMA, by investigating how to improve the probabilistic selection of chaotic operators based on the maximum Lyapunov exponent (MLE), an inherent property of chaotic maps. We implement the comparison between MCSMA with other state-of-the-art methods on IEEE Congress on Evolution Computation (CEC) i.e., CEC2017 benchmark test suits and CEC2011 practical problems to demonstrate its potency and perform dendritic neuron model training to test the robustness of MCSMA on classification problems. Finally, the parameters' sensitivities of MCSMA, the utilization of the solution space, and the effectiveness of the MLE are adequately discussed.

Multi-objective combinatorial optimization analysis of the recycling of retired new energy electric vehicle power batteries in a sustainable dynamic reverse logistics network

The recycling of retired new energy vehicle power batteries produces economic benefits and promotes the sustainable development of environment and society. However, few attentions have been paid to the design and optimization of sustainable reverse logistics network for the recycling of retired power batteries. To this end, we develop a six-level sustainable dynamic reverse logistics network model from the perspectives of economy, environment, and society. We solve the multi-objective combinatorial optimization model to explore the layout of the sustainable reverse logistics network for retired new energy vehicle power batteries recycling. A case study is implemented to verify the effectiveness of the proposed model. The results show that (a) the facility nodes near the front of the network fluctuate more by opening and closing; (b) the dynamic reverse logistics network is superior to its static counterpart; and (c) cooperation cost changes affect the transaction volume between third-party and cooperative enterprises and total network cost.

An Improved Multi-Objective Particle Swarm Optimization Algorithm Based on Angle Preference

Multi-objective particle swarm optimization (MOPSO) algorithms based on angle preference provide a set of preferred solutions by incorporating a user’s preference. However, since the search mechanism is stochastic and asymmetric, traditional MOPSO based on angle preference are still easy to fall into local optima and lack enough selection pressure on excellent individuals. In this paper, an improved MOPSO algorithm based on angle preference called IAPMOPSO is proposed to alleviate those problems. First, to create a stricter partial order among the non-dominated solutions, reference vectors are established in the preference region, and the adaptive penalty-based boundary intersection (PBI) value is used to update the external archive. Second, to effectively alleviate the swarm to fall into local optima, an adaptive preference angle is designed to increase the diversity of the population. Third, neighborhood individuals are selected for each particle to update the individual optimum to increase the information exchange among the particles. With the proposed angle preference-based external archive update strategy, solutions with a smaller PBI are given higher priority to be selected, and thus the selection pressure on excellent individuals is enhanced. In terms of an increase in the diversity of the population, the adaptive preference angle adjustment strategy that gradually narrows the preferred area, and the individual optimum update strategy which updates the individual optimum according to the information of neighborhood individuals, are presented. The experimental results on the benchmark test functions and GEM data verify the effectiveness and efficiency of the proposed method.