Souvik Kundu

Synergizing fitness learning with proximity-based food source selection in artificial bee colony algorithm for numerical optimization

Evolutionary computation (EC) paradigm has undergone extensions in the recent years diverging from the natural process of genetic evolution to the simulation of natural life processes exhibited by the living organisms. Bee colonies exemplify a high level of intrinsic interdependence and co-ordination among its members, and algorithms inspired from the bee colonies have gained recent prominence in the field of swarm based metaheuristics. The artificial bee colony (ABC) algorithm was recently developed, by simulating the minimalistic foraging model of honeybees in search of food sources, for solving real-parameter, non-convex, and non-smooth optimization problems. The single parameter perturbation in classical ABC resulted in fairly commendable performance for simple problems without epistasis of variables (separable). However, it suffered from narrow search zone and slow convergence which eventually led to poor exploitation tendency. Even with the increase in dimensionality, a significant deterioration was observed in the ability of ABC to locate the optimum in a huge search volume. Some of the probable shortcomings in the basic ABC approach, as observed, are the single parameter perturbation instead of a multiple one, ignoring the fitness to reward ratio while selecting food sites, and most importantly the absence of environmental factors in the algorithm design. Research has shown that spatial environmental factors play a crucial role in insect locomotion and foragers seem to learn the direction to be undertaken based on the relative analysis of its proximal surroundings. Most importantly, the mapping of the forager locomotion from three dimensional search spaces to a multidimensional solution space calls forth the implementation of multiple modification schemes. Based on the fundamental observation pertaining to the dynamics of ABC, this article proposes an improved variant of ABC aimed at improving the optimizing ability of the algorithm over an extended set of problems. The hybridization of the proposed fitness learning mechanism with a weighted selection scheme and proximity based stimuli helps to achieve a fine blending of explorative and exploitative behaviour by enhancing both local and global searching ability of the algorithm. This enhances the ability of the swarm agents to detect optimal regions in the unexplored fitness basins. With respect to its immediate surroundings, a proximity based component is added to the normal positional modification of the onlookers and is enacted through an improved probability selection scheme that takes the T/E (total reward to distance) ratio metric into account. The biologically-motivated, hybridized variant of ABC achieves a statistically superior performance on majority of the tested benchmark instances, as compared to some of the most prominent state-of-the-art algorithms, as is demonstrated through a detailed experimental evaluation and verified statistically.

Inducing Niching Behavior in Differential Evolution Through Local Information Sharing

In practical situations, it is very often desirable to detect multiple optimally sustainable solutions of an optimization problem. The population-based evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations to aid the parallel localized convergence of population members around different basins of attraction. This paper presents an improved information-sharing mechanism among the individuals of an evolutionary algorithm for inducing efficient niching behavior. The mechanism can be integrated with stochastic real-parameter optimizers relying on differential perturbation of the individuals (candidate solutions) based on the population distribution. Various real-coded genetic algorithms (GAs), particle swarm optimization (PSO), and differential evolution (DE) fit the example of such algorithms. The main problem arising from differential perturbation is the unequal attraction toward the different basins of attraction that is detrimental to the objective of parallel convergence to multiple basins of attraction. We present our study through DE algorithm owing to its highly random nature of mutation and show how population diversity is preserved by modifying the basic perturbation (mutation) scheme through the use of random individuals selected probabilistically. By integrating the proposed technique with DE framework, we present three improved versions of well-known DE-based niching methods. Through an extensive experimental analysis, a statistically significant improvement in the overall performance has been observed upon integrating of our technique with the DE-based niching methods.

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 parent-centric mutation with normalized neighborhoods for inducing niching behavior in differential evolution.

In real life, we often need to find multiple optimally sustainable solutions of an optimization problem. Evolutionary multimodal optimization algorithms can be very helpful in such cases. They detect and maintain multiple optimal solutions during the run by incorporating specialized niching operations in their actual framework. Differential evolution (DE) is a powerful evolutionary algorithm (EA) well-known for its ability and efficiency as a single peak global optimizer for continuous spaces. This article suggests a niching scheme integrated with DE for achieving a stable and efficient niching behavior by combining the newly proposed parent-centric mutation operator with synchronous crowding replacement rule. The proposed approach is designed by considering the difficulties associated with the problem dependent niching parameters (like niche radius) and does not make use of such control parameter. The mutation operator helps to maintain the population diversity at an optimum level by using well-defined local neighborhoods. Based on a comparative study involving 13 well-known state-of-the-art niching EAs tested on an extensive collection of benchmarks, we observe a consistent statistical superiority enjoyed by our proposed niching algorithm.

A modified differential evolution-based combined routing and sleep scheduling scheme for lifetime maximization of wireless sensor networks

In recent years, wireless sensor networks (WSNs) have transitioned from being objects of academic research interest to a technology that is frequently employed in real-life applications and rapidly being commercialized. Nowadays the topic of lifetime maximization of WSNs has attracted a lot of research interest owing to the rapid growth and usage of such networks. Research in this field has two main directions into it. The first school of researchers works on energy efficient routing that balances traffic load across the network according to energy-related metrics, while the second school of researchers takes up the idea of sleep scheduling that reduces energy cost due to idle listening by providing periodic sleep cycles for sensor nodes. As energy efficiency is a very critical consideration in the design of low-cost sensor networks that typically have fairly low node battery lifetime, this raises the need for providing periodic sleep cycles for the radios in the sensor nodes. Until now, these two fields have remained more or less disjoint leading to designs where to optimize one component, the other one must be pre-assumed. This in turn leads to many practical difficulties. To circumvent such difficulties in the performance of sensor networks, instead of separately solving the problem of energy efficient routing and sleep scheduling for lifetime maximization, we propose a single optimization framework, where both the components get optimized simultaneously to provide a better network lifetime for practical WSN. The framework amounts to solving a constrained non-convex optimization problem by using the evolutionary computing approach, based on one of the most powerful real-parameter optimizers of current interest, called Differential Evolution (DE). We propose a DE variant called modified semi-adaptive DE (MSeDE) to solve this optimization problem. The results have been compared with two state-of-the-art and widely used variants of DE, namely JADE and SaDE, along with one improved variant of the Particle Swarm Optimization (PSO) algorithm, called comprehensive learning PSO (CLPSO). Moreover, we have compared the performance of MSeDE with a well-known constrained optimizer, called

Utilizing time-linkage property in DOPs: An information sharing based Artificial Bee Colony algorithm for tracking multiple optima in uncertain environments

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Crowding-based local differential evolution with speciation-based memory archive for dynamic multimodal optimization

ε-constrained DE with an archive and gradient-based mutation that ranked first in the competition on real-parameter constrained optimization, held under the 2010 IEEE Congress on Evolutionary Computation (CEC). Again to demonstrate the effectiveness of the optimization framework under consideration, we have included results obtained with a separate routing and sleep scheduling method in our comparative study. Our simulation results indicate that in all test cases, MSeDE can outperform the competitor algorithms by a good margin.

Teaching and learning best Differential Evoltuion with self adaptation for real parameter optimization

This paper presents a novel search metaheuristic inspired from the physical interpretation of the optic flow of information in honeybees about the spatial surroundings that help them orient themselves and navigate through search space while foraging. The interpreted behavior combined with the minimal foraging is simulated by the artificial bee colony algorithm to develop a robust search technique that exhibits elevated performance in multidimensional objective space. Through detailed experimental study and rigorous analysis, we highlight the statistical superiority enjoyed by our algorithm over a wide variety of functions as compared to some highly competitive state-of-the-art methods.