MOSDA: A proposal for multiple objective spiral dynamics algorithm

This paper proposed a multi-objective spiral dynamic algorithm (MOSDA) to solve multiple objectives problems. SDA is originally a single objective optimizer that inspired based on the spiral phenomena in nature. It has a good elitism strategy and has a simple structure. A method called “archive method” that is used in multi-objective particle swarm optimization (MOPSO) is adopted into SDA to develop its multiobjective (MO) type algorithm. Moreover, MOSDA is formulated by applying the widely-used concept of Pareto dominance to determine the movement of the particles and at the same time, the algorithm maintains the non-dominated solution in a setup global repository. These non-dominated solutions then will be used to guide other particles to move. The proposed algorithm is tested with several benchmark functions for multi-objective problems. Pareto front (PF) graphs are presented as the results of these tests. The accuracy and diversity of the produced PF are highly competitive compared to MOPSO

A multi-objective Spiral Dynamic algorithm and its application for PD design

This paper presents a novel multi-objective Spiral Dynamic Optimization (MOSDA) algorithm. It is an extended version of a single objective type SDA. A Non-dominated sorting (NS) approach from Non-dominated Sorting Genetic Algorithm II (NSGAII) is adopted into SDA to develop its multi-objective (MO) type algorithm. SDA has a good elitism strategy and a simple structure. On the other hand, NS is a fast strategy to develop good Pareto Front (PF) characteristics for MO type algorithm. The proposed algorithm is tested with various benchmark functions used to test a newly developed MO algorithm. A PF graph is presented as a result of the test. Moreover, it is adopted to optimize parameters of Proportional- Derivative (PD) controller for an Inverted Pendulum (IP) system. Time-domain response of the IP is presented to show performance of the optimized controller. Result presented in this paper shows that MOSDA has a better performance in terms of finding PF and solution spread when tested with benchmark functions compared to NSGAII. In terms of its application in solving a real problem, both algorithms successfully optimize the PD and control the system very well. IP controlled by MOSDA- based PD shows better rise time