Shantanab Debchoudhury

Co-evolving bee colonies by forager migration: A multi-swarm based Artificial Bee Colony algorithm for global search space

Abstract Swarm intelligent algorithms focus on imitating the collective intelligence of a group of simple agents that can work together as a unit. Such algorithms have particularly significant impact in the fields like optimization and artificial intelligence (AI). This research article focus on a recently proposed swarm-based metaheuristic called the Artificial Bee Colony (ABC) algorithm and suggests modification to the algorithmic framework in order to enhance its performance. The proposed ABC variant shall be referred to as Migratory Multi-swarm Artificial Bee Colony (MiMSABC) algorithm. Different perturbation schemes of ABC function differently in varying landscapes. Hence to maintain the basic essence of all these schemes, MiMSABC deploys a multiple swarm populations that are characterized by different and unique perturbation strategies. The concept of reinitializing foragers around a depleted food source using a limiting parameter, as often used conventionally in ABC algorithms, has been avoided. Instead a performance based set of criteria has been introduced to thoroughly detect subpopulations that have shown limited progress to eke out the global optimum. Once failure is detected in a subpopulation provisions have been made so that constituent foragers can migrate to a better performing subpopulation, maintaining, however, a minimum number of members for successful functioning of a subpopulation. To evaluate the performance of the algorithm, we have conducted comparative study involving 8 algorithms for testing the problems on 25 benchmark functions set proposed in the Special Session on IEEE Congress on Evolutionary Competition 2005. Thorough a detailed analysis we have highlighted the statistical superiority of our proposed MiMSABC approach over a set of population based metaheuristics.

An improved particle swarm optimizer with difference mean based perturbation

Concept of the particle swarms emerged from a simulation of the collective behavior of social creatures and gradually evolved into a powerful global optimization technique, now well-known as the Particle Swarm Optimization (PSO). PSO is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. The very basic PSO model does not ensure convergence to an optimal solution and it also suffers from its dependency on external parameters like acceleration parameters and inertia weight. Owing to its comparatively poor efficiency, a multitude of measures has been taken by the researchers to improve the performance of PSO. This paper presents a scheme to modify the very basic framework of PSO by the introduction of a novel dimensional mean based perturbation strategy, a simple aging guideline, and a set of nonlinearly time-varying acceleration coefficients to achieve a better tradeoff between explorative and exploitative tendencies and thus to avoid premature convergence on multimodal fitness landscapes. The aging guideline is used to introduce fresh solutions in the swarm when particles show no further improvement. A systematically rendered comparison between the proposed PSO framework and several other state-of-the-art PSO-variants as well as evolutionary algorithms on a test-suite comprising 16 standard numerical benchmarks and two real world problems indicates that the proposed algorithm can enjoy a statistically superior performance on a wide variety of problems.

Modified Differential Evolution with Locality induced Genetic Operators for dynamic optimization

This article presents a modified version of the Differential Evolution (DE) algorithm for solving Dynamic Optimization Problems (DOPs) efficiently. The algorithm, referred as Modified DE with Locality induced Genetic Operators (MDE-LiGO) incorporates changes in the three basic stages of a standard DE framework. The mutation phase has been entrusted to a locality-induced operation that retains traits of Euclidean distance-based closest individuals around a potential solution. Diversity maintenance is further enhanced by inclusion of a local-best crossover operation that empowers the algorithm with an explorative ability without directional bias. An exhaustive dynamic detection technique has been introduced to effectively sense the changes in the landscape. An even distribution of solutions over different regions of the landscape calls for a solution retention technique that adapts this algorithm to dynamism by using the previously stored information in diverse search domains. MDE-LiGO has been compared with seven state-of-the-art evolutionary dynamic optimizers on a set of benchmarks known as the Generalized Dynamic Benchmark Generator (GDBG) used in competition on evolutionary computation in dynamic and uncertain environments held under the 2009 IEEE Congress on Evolutionary Computation (CEC). The experimental results clearly indicate that MDE-LiGO can outperform other algorithms for most of the tested DOP instances in a statistically meaningful way.

Adaptive Differential Evolution with Locality based Crossover for Dynamic Optimization

Real life problems which deal with time varying landscape dynamics often pose serious challenge to the mettle of researchers in the domain of Evolutionary Computation. Classified as Dynamic Optimization problems (DOPs), these deal with candidate solutions which vary their dominance over dynamic change instances. The challenge is to efficiently recapture the dominant solution or the global optimum in each varying landscape. Differential Evolution (DE) algorithm with modifications of adaptability have been widely used to deal with the complexities of a dynamic landscape, yet problems persist unless dedicated structuring is done to exclusively deal with DOPs. In Adaptive Differential Evolution with Locality based Crossover (ADE-LbX) the mutation operation has been entrusted to a locality based scheme that retains traits of Euclidean distance based closest individuals around a potential solution. Diversity maintenance is further enhanced by incorporation of local best crossover scheme that renders the landscape independent of direction and empowers the algorithm with an explorative ability. An even distribution of solutions in different regions of landscape calls for a solution retention technique that adapts this algorithm to dynamism by using its previous information in diverse search domains. To evaluate the performance of ADE-LbX, it has been tested over Dynamic Problem instance proposed as in CEC 09 and compared with State-of-the-arts. The algorithm enjoys superior performance in varied problem configurations of the problem.

Improved CMA-ES with Memory based Directed Individual Generation for Real Parameter Optimization

Covariance Matrix Adaptation and Evolution Strategy (CMA-ES) is an efficient method of optimization that iteratively generates new individuals around an ever-adaptive recombination point. Although it ensures speed and high rate of exploitation, CMA-ES suffers a major drawback as the scheme of generating new members scattered around an influential mean may often lead to members drawn to local minima. The result is that while precision of better solutions increases, the ability to reform is lost. In this paper we incorporate a directional feature to the generation wise perturbation of individuals in standard version of CMA-ES that utilizes potentially useful information from previous generation to retain the influence of old recombination point. Coupled with a modified population size we attempt to form an algorithm that amalgamates the effectiveness of CMA-ES along with the ability to explore. The performance is tested on IEEE CEC (Congress on Evolutionary Computation) 2013 Special Session on Real-Parameter Optimization in 10, 30 and 50 dimensions. The results obtained clearly indicates that the proposed algorithm addressed as CMA-ES with Memory based Directed Individual Generation (CMA-ES-DIG) is able to perform excessively well on majority of the test cases in a statistically meaningful way.

A Novel Improved Discrete ABC Algorithm for Manpower Scheduling Problem in Remanufacturing

Remanufacturing technique is a widely used approach in modern industries. But the very first step of this technique is disassembling. This disassembling operation requires an efficient employee pool and their allocation to several steps of disassembling. In this paper, we have proposed a improved ABC algorithm that can be used to solve the manpower scheduling problem for the disassembling operation in remanufacturing industry. We test this algorithm on several instances along with some existing state-of-art algorithms. The results prove the efficiency of this algorithm to solve manpower scheduling problem in remanufacturing.

Modified estimation of Distribution algorithm with differential mutation for constrained optimization

Estimation of Distribution algorithms (EDAs) are probabilistic-model based optimization techniques that exploit promising solution candidates by developing particles around them in accordance to a pre-specified distribution. This paper attempts to approach constrained optimization problems by an interdependent parallel functioning of a modified Gaussian distribution based EDA with differential mutation on the lines of rand/1 perturbation scheme. A modified penalty function free from scaling parameters has been proposed to deal with the constraints associated. The results have been collected from functional landscapes defined by the CEC 2010 benchmark and have been compared with existing state-of-the-art methods for constrained optimization.

Multipopulation based differential evolution with self exploitation strategy

In this article a multi-population based DE-variant has been proposed to tackle DOPs. The algorithm, denoted as MPBDE-SES uses a self exploitative scheme along with classical DE. Moreover it also uses Brownian and Quantum individuals. An aging mechanism has been incorporated to get rid of stagnation. Apart from this exclusion principle, repulsion scheme and a recombination based mutation strategy causes uniform distribution of the subpopulation over the entire search space which enhances the tracking ability of the algorithm. Performance of MPBDE-SES has been tested over the suite of benchmark problems used in Competition on Evolutionary Computation in Dynamic and Uncertain Environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC) and compared with six state-of-the-art EAs. The results obtained clearly and statistically outperform the other algorithms.

Noise-induced errors in geophysical parameter estimation from retarding potential analyzers in low Earth orbit

Retarding Potential Analyzers (RPA) have a rich flight heritage. Standard curve-fitting analysis techniques exist that can infer state variables in the ionospheric plasma environment from RPA data, but the estimation process is prone to errors arising from a number of sources. Previous work has focused on the effects of grid geometry on uncertainties in estimation; however, no prior study has quantified the estimation errors due to additive noise. In this study, we characterize the errors in estimation of thermal plasma parameters by adding noise to the simulated data derived from the existing ionospheric models. We concentrate on low-altitude, mid-inclination orbits since a number of nano-satellite missions are focused on this region of the ionosphere. The errors are quantified and cross-correlated for varying geomagnetic conditions.

Short-term hydro-thermal scheduling using CMA-ES with directed target to best perturbation scheme

Covariance matrix adaptation evolution strategy with directed target to best perturbation CMS-ES_DTBP scheme is applied for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are also considered. The feasibility, and effectiveness of the proposed algorithm is demonstrated through a test system, and the obtained results are compared with the existing conventional and evolutionary algorithms. Simulation results reveal that the proposed CMS-ES_DTBP scheme appears to be best in terms of convergence speed and cost compared with other techniques.