The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi- mission waveform design

Abstract

This paper furthers the development of the application of Evolutionary Computation, specifically Genetic Algorithms (GAs) to the design of simultaneously transmitted orthogonal waveforms. The goal of the application is to determine a suite of "optimal" waveforms (in the Pareto sense) for a single platform radar system performing multiple radar missions simultaneously. The waveform suite is determined by applying the Strength Pareto Evolutionary Algorithm 2 (SPEA2) developed by Zitzler, Laumanns & Theile [1] to find waveform parameters that successfully realize a set of objectives particular to a variety of radar missions. The objectives to optimize are dictated by the particular missions of interest. The mapping of these objective functions to actual radar performance parameters is used in the SPEA2 algorithm to determine how best to simultaneously perform multiple radar missions such as GMTI, AMTI, SAR etc. using a single radar system in a Pareto optimal sense. Preliminary results are presented for a scaled down multimission multi-objective function scenario.

Multiobjective Game Method Based on Self‐Adaptive Space Division of Design Variables and Its Application to Vehicle Suspension

“…Evolutionary algorithms (such as the nondominated sorting genetic algorithm-II (NSGA-II) [1] and strength Pareto evolutionary algorithm 2 (SPEA-2)) [2] are popular approaches to generating Pareto optimal solutions to a multiobjective optimization problem.…”

“…(1) The design variables are decomposed into the strategy space owned by each player and the original highdimensional optimization problem is transformed into multiple low-dimensional optimization problems, which can reduce the complexity of problem. (2) Designer can clearly know the correlation between design variables and the objective functions. (3) Optimization objectives are considered to be the different game players and optimization results are seen as game players' mutual negotiation and compromise.…”

“…Mathematical Problems in Engineering 7 Table 3: Impact factors of (2) affecting the objective functions. Table 4: Impact factors of (3) affecting the objective functions.…”

“…Mathematical Problems in Engineering 9 The game equilibrium solutions are (2) Impact factors of (2) affecting the objective functions are shown in Table 3 Impact factors of (3) affecting the objective functions are shown in Table 4. ] consist of coordinate values of three convergent "means. "…”

Using genetic algorithms for radar waveform selection

“…However, they have been successfully implemented in areas such as radar multi-mission waveform design [14] and phased array antenna design [15]. In this paper we examine using the radar ambiguity function for monostatic, bistatic and multistatic configurations and a genetic algorithm as a design tool.…”