Tarun Kumar Sharma

Shuffled artificial bee colony algorithm

In this study, we have introduced a hybrid version of artificial bee colony (ABC) and shuffled frog-leaping algorithm (SFLA). The hybrid version is a two-phase modification process. In the first phase to increase the global convergence, the initial population is produced using randomly generated and chaotic system, and then in the second phase to balance two antagonist factors, i.e., exploration and exploitation capabilities, population is portioned into two groups (superior and inferior) based on their fitness values. ABC is applied to the first group, whereas SFLA is applied to the second group of population. The proposed version is named as Shuffled-ABC. The proposal is implemented and tested on constrained benchmark consulted from CEC 2006 and five chemical engineering problems where constraints are handled using penalty function methods. The simulated results illustrate the efficacy of the proposal.

Identification of noise in multi noise plant using enhanced version of shuffled frog leaping algorithm

AbstractIn any factory or industry the high level noise can be very harmful to the employees. As investigated by Occupational Safety and Health Act of 1970, the high level noise not only causes physiological ailments in employees but also causes harmful environment in the neighborhood. Therefore it becomes essential to control the noise levels in any manufacturing plant or industry. This can be achieved by optimal allocation of noise equipment which is quite not easy to recognize the exact location. In this study a shuffled frog-leaping algorithm (SFLA) with modification is applied to identify optimal locations for equipment in order to reduce noise level in multi noise plant. Comparatively, SFLA is a recent addition to the family of nontraditional population based search methods that mimics the social and natural behavior of species (frogs). SFLA merges the advantages of particle swarm optimization and genetic algorithm (GA). Though SFLA has been successfully applied to solve many benchmark and real time problems but it limits in convergence speed. In order to improve its performance, the frog with best position in each memeplexes is allowed to slightly modify its position using random walk. This process improves the local search around the best position. The proposal is named as improved local search in SFLA. The simulated results defend the efficacy of the proposal when compared with the differential evolution, GA and SFL algorithms.n

Changing factor based food sources in artificial bee colony

The present study, proposes an optimization algorithm for solving the continuous global optimization problems. The basic framework selected for modeling the algorithm is Artificial Bee Colony (ABC). The proposed variant is called ABC with changing factor or CF-ABC. The proposed CF-ABC tries to maintain a tradeoff between exploration and exploitation so as to obtain reasonably good results. The proposed algorithm is implemented on the six benchmark functions and four engineering design problems. Simulated results illustrate the efficiency of the CF-ABC in terms of convergence speed and mean value.

Shuffled Frog Leaping Algorithm with Adaptive Exploration

Shuffled frog leaping algorithm is a nature inspired memetic stochastic search method which is gaining the focus of researchers since it was introduced. SFLA has the limitation that its convergence speed decreases towards the later stage of execution and it also tends to stuck into local extremes. To overcome such limitations, this paper first proposes a variant in which a few new random frogs are generated and the worst performing frogs population are replaced by them. Experimental results show that a high number of replaced frogs does not always provide better results. As the execution progresses the optimized number of replaced frogs decreases. Based on the experimental observations, the paper then proposes another variant in which the number of replaced frogs adapts to the stage of the execution and hence provides the best results regardless of the stage of execution. Experiments are carried out on five benchmark test functions.

Role of decision tree in supplementing tacit knowledge for Hypothetico-Deduction in higher education

With a notion to create a knowledge centric environment, this paper substantiates the inclusion of data mining technique of decision tree for supplementing Hypothetico-Deductive methodology. Presently tacit knowledge plays an important role in the formulation of testable hypothesis from a theoretical framework of dependent and independent variables, identified for the system. The introduction of decision tree in Hypothetico-Deductive methodology concretizes a path towards knowledge creation. The case of a higher education institution is considered in particular.

Opposition learning based phases in artificial bee colony

Artificial bee colony (ABC) is a recently introduced swarm intelligence algorithm (SIA). Initially only unconstrained problems were handled by ABC, which was later modified by embedding one more parameter called modified rate to handle constrained problems. Since then, ABC and its variants have shown a remarkable success in the domain of swarm intelligence optimization algorithms. The exploration capability of ABC is comparatively better than exploitation which sometimes limits the convergence rate of ABC while handling multimodal optimization problems. In this study the foraging process of two phases has been enhanced by embedding opposition based learning concept. This modification is introduced to enhance the acceleration and exploitation capability of ABC. The variant is named as O-ABC (Opposition based ABC). The efficiency of O-ABC is initially evaluated on 12 benchmark functions consulted from literature. Later O-ABC is applied for intrusion detection. The simulated comparative results have shown the competitiveness of the proposal.

Knowledge building through optimized classification rule set generation using genetic based elitist multi objective approach

Knowledge building is an important activity taken up by various organizations. The paper exemplifies the creation of a knowledge-centric environment for a non-profit sector such as a higher education. Building knowledge and thereafter using it are important aspects of knowledge-centric environment; this further helps the organization to gain competitive advantage. With the increase in popularity of genetic algorithm (GA), the technique has been used in building efficient classifiers for creating effective rule sets. The paper makes use of multi-objective genetic algorithm for building GA-based efficient classifier because classification rule mining is itself, a multi-objective problem. Knowledge expressed through classification rules help in establishing relationships between attributes that are not visible openly. The study assumes importance as curriculum planning is an important aspect of any academic institution, the knowledge derived in the form of rules residing in the knowledge base help to substantiate proper curriculum development, making a sizeable contribution toward professional growth and advancement of the students. On implementation of the findings, educational organizations will be able to institute themselves as knowledge centric.

Opposition based learning ingrained shuffled frog-leaping algorithm

Abstract Shuffled frog-leaping algorithm (SFLA) is a kind of memetic algorithm. Randomicity and determinacy, the two keywords of SFLA ensures flexibility, robustness and exchange of information effectively in SFLA. In the basic structure of SFLA, the frogs are divided into memeplexes based on their fitness values where they forage for food. In this study the opposition based learning concept is embedded into the memeplexes before the frog initiates foraging. The proposal is investigated, analyzed and compared with latest variants of SFLA on benchmark functions (unimodal and multimodal) along with a real life problem. The result analysis shows that the proposed variant performs consistently well for different types of problems considered in this study.

Improved Local Search in Shuffled Frog Leaping Algorithm

Shuffled frog-leaping algorithm (SFLA) is comparatively a recent addition to the family of nontraditional population-based search methods that mimics the social and natural behavior of species (frogs). SFLA merges the advantages of particle swarm optimization (PSO) and genetic algorithm (GA). Though SFLA has been successfully applied to solve many benchmark and real-time problems it limits the convergence speed. In order to improve its performance, the frog with the best position in each memeplexes is allowed to slightly modify its position using random walk. This process improves the local search around the best position. The proposal is named improved local search in SFLA (ILS-SFLA). For validation, three engineering optimization problems are consulted from the literature. The simulated results defend the efficacy of the proposal when compared to state-of-the-art algorithms.

Asynchronous Differential Evolution with Convex Mutation

Asynchronous differential evolution (ADE) is recently introduced variant of differential evolution (DE). In ADE the mutation, crossover, and selection operations are performed asynchronously whereas in DE these operations are performed synchronously. This asynchronous process helps in good exploration and well suited for parallel optimization. In this study the strength of ADE is enhanced by incorporating convex mutation. Convex mutation efficiently utilizes the information of the parents which assists in faster convergence. The proposal is named ADE–CM. The potential of the proposal is evaluated and compared with state-of-the-art algorithms over a selected noisy benchmark functions consulted from the literature.