Original Invasive Weed Optimization Research Articles

MOEA/D with Adaptive IWO for Synthesizing Phase-Only Reconfigurable Linear Arrays

In order to well solve the phase-only reconfigurable arrays synthesis problems, we introduce an adaptive strategy in invasive weed optimization (IWO), and integrate the adaptive IWO (AIWO) into the framework of MOEA/D, a popular multi-objective algorithm. Then, a new version of MOEA/D with adaptive IWO, named MOEA/D-AIWO is proposed in this paper for solving the synthesis problems. In MOEA/D-AIWO, the proposed adaptive strategy is adopted for improving search ability and balancing diversity and convergence. We introduce an adaptive standard deviation, which changes not only with the increase of evolution generations, but also exponentially with the fitness function value of each individual. This strategy improves the convergence rate and helps the seeds escape from local optimum. Taking advantage of the powerful searching ability of invasive weeds and well framework of MOEA/D, the overall performance of the proposed MOEA/D-AIWO is illustrated in solving two sets of phase-only reconfigurable arrays synthesis problems. Comparing results with MOEA/D-IWO (MOEA/D with original IWO) and MOEA/D-DE are also provided in this paper.

Invasive weed optimization for solving index tracking problems

Index tracking belongs to one of the most important types of problems in portfolio management. In contrast to classical (active) portfolio management and optimization, passive portfolio management usually seeks to replicate a given index for a financial market. Due to transaction costs or legal or other practical limitation on the tradability of the respective assets, such indexes are often not fully replicated by a respective portfolio. Instead, one seeks to use a subset of the index assets (or other types of assets) to obtain a portfolio most similar to the index. This index tracking problem can be formulated as an optimization problem with respect to the minimization of the tracking error. In this article, we explore possibilities to solve the index tracking problem with invasive weed optimization (IWO), a rather new population-based metaheuristics algorithm. The complexity of this real-life problem and especially its solution space and restrictions require substantial adaptation of the original IWO algorithm. We explore different possibilities to adapt IWO to the considered type of problem. The adapted IWO method is tested using MSCI USA Value data, and systematic studies to find suitable parameter values are conducted. Although the method basically works well, the obtained results do not fully reach our intended benchmark. Reasons for that and possibilities for further improvement of the methodology are discussed.

Synthesis of Phase-Only Reconfigurable Linear Arrays Using Multiobjective Invasive Weed Optimization Based on Decomposition

Synthesis of phase-only reconfigurable array aims at finding a common amplitude distribution and different phase distributions for the array to form different patterns. In this paper, the synthesis problem is formulated as a multiobjective optimization problem and solved by a new proposed algorithm MOEA/D-IWO. First, novel strategies are introduced in invasive weed optimization (IWO) to make original IWO fit for solving multiobjective optimization problems; then, the modified IWO is integrated into the framework of the recently well proved competitive multiobjective optimization algorithm MOEA/D to form a new competitive MOEA/D-IWO algorithm. At last, two sets of experiments are carried out to illustrate the effectiveness of MOEA/D-IWO. In addition, MOEA/D-IWO is compared with MOEA/D-DE, a new version of MOEA/D. The comparing results show the superiority of MOEA/D-IWO and indicate its potential for solving the antenna array synthesis problems.

A differential invasive weed optimization algorithm for improved global numerical optimization

Hybridization in context to Evolutionary Computation (EC) aims at combining the operators and methodologies from different EC paradigms to form a single algorithm that may enjoy a statistically superior performance on a wide variety of optimization problems. In this article we propose a simple but very efficient hybrid evolutionary algorithm that embeds the difference vector based mutation scheme of Differential Evolution (DE) into another recently developed global optimization algorithm known as Invasive Weed Optimization (IWO). IWO emulates the ecological behavior of the colonizing weeds. The hybrid algorithm, referred by us as Differential Invasive Weed Optimization (DIWO), is shown to possess greater explorative power as compared to the original DE and original IWO, through an analysis of the change of population-variance of these algorithms over successive generations and also through empirical simulations. We compare DIWO with original IWO, a modified IWO, two best known DE-variants: SaDE and JADE, and two state-of-the-art real optimizers: G-CMA-ES (Restart Covariance Matrix Adaptation Evolution Strategy with increasing population size) and DMS-PSO (Dynamic Multi Swarm Particle Swarm Optimization) over a test-suite of 25 shifted, rotated, and compositional benchmark functions and also one engineering optimization problem. Our comparative study indicates that although the hybridization scheme does not impose any serious burden on DIWO in terms of number of Function Evaluations (FEs), DIWO still enjoys a statistically superior performance over most of the tested benchmarks and especially over the multi-modal, rotated, and compositional ones in comparison to the other algorithms considered here.

Design of Non-Uniform Circular Antenna Arrays Using a Modified Invasive Weed Optimization Algorithm

An ecologically inspired optimization algorithm, called invasive weed optimization (IWO), is presented for the design of non-uniform, planar, and circular antenna arrays that can achieve minimum side lobe levels for a specific first null beamwidth while avoiding the mutual coupling effects simultaneously. IWO recently emerged as a derivative-free real parameter optimizer that mimics the ecological behavior of colonizing weeds. For the present application, classical IWO has been modified by introducing a more explorative routine of changing the standard deviation of the seed population (equivalent to mutation step-size in evolutionary algorithms) of the algorithm. Simulation results over three significant instances of the circular array design problem have been presented to illustrate the effectiveness of the modified IWO algorithm. The design results obtained with modified IWO have been shown to comfortably beat those obtained with other state-of-the-art metaheuristics like genetic algorithm (GA), particle swarm optimization (PSO), original IWO and differential evolution (DE) in a statistically meaningful way.