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

Abstract

Particle swarm optimization system based on the distributed architecture has shown its efficiency for static optimization and has not been studied to solve dynamic multiobjective problems (DMOPs). When solving DMOP, tracking the best solutions over time and ensuring good exploitation and exploration are the main challenging tasks. This study proposes a novel Dynamic Pareto bi-level Multi-Objective Particle Swarm Optimization (DPb-MOPSO) algorithm including two parallel optimization levels. At the first level, all solutions are managed in a single search space. When a dynamic change is successfully detected, the Pareto ranking operator is used to enable a multiswarm subdivisions and processing which drives the second level of enhanced exploitation. A dynamic handling strategy based on random detectors is used to track the changes of the objective function due to time-varying parameters. A response strategy consisting in re-evaluate all unimproved solutions and replacing them with newly generated ones is also implemented. Inverted generational distance, mean inverted generational distance, and hypervolume difference metrics are used to assess the DPb-MOPSO performances. All quantitative results are analyzed using Friedman's analysis while the Lyapunov theorem is used for stability analysis. Compared with several multi-objective evolutionary algorithms, the DPb-MOPSO is robust in solving 21 complex problems over a range of changes in both the Pareto optimal set and Pareto optimal front. For 13 UDF and ZJZ functions, DPb-MOPSO can solve 8/13 and 7/13 on IGD and HVD with moderate changes. For the 8 FDA and dMOP benchmarks, DPb-MOPSO was able to resolve 4/8 with severe change on MIGD, and 5/8 for moderate and slight changes. However, for the 3 kind of environmental changes, DPb-MOPSO assumes 4/8 of the solving function on IGD and HVD. <br>

A Distributed Multifactorial Particle Swarm Optimization Approach

“…Unlike using Swarm Intelligence (SI) methods [3], [4] and Evolutionary Algorithms (EA) [5], [6] to solve a single task from scratch, the main challenge of Multi-Objective Evolutionary Algorithms (MOEAs) is to optimize multiple independent tasks simultaneously where a task may stand for a static single/multi-objective problem (SOP, MOP) or a Many-Objective Optimization Problem (MaOP) or a dynamic multi-objective problem (DMOP) [7]- [11]. In [1], Gupta et al introduced MFO for multi-task optimization, which is also known as Multi-Objective Multitasking Optimization (MTO).…”

A Distributed Multifactorial Particle Swarm Optimization Approach

“…Unlike using Swarm Intelligence (SI) methods [3], [4] and Evolutionary Algorithms (EA) [5], [6] to solve a single task from scratch, the main challenge of Multi-Objective Evolutionary Algorithms (MOEAs) is to optimize multiple independent tasks simultaneously where a task may stand for a static single/multi-objective problem (SOP, MOP) or a Many-Objective Optimization Problem (MaOP) or a dynamic multi-objective problem (DMOP) [7]- [11]. In [1], Gupta et al introduced MFO for multi-task optimization, which is also known as Multi-Objective Multitasking Optimization (MTO).…”