Incorporating logic in Artificial Bee Colony (ABC) algorithm to solve first order logic problems: The logical ABC

Abstract

The fascination for generating reasons and drawing inferences has given a tremendous impetus to research in theoretical computer science. In spite of having well defined constructs and globally accepted notations for logic and Firstorder theorem provers, theorem proving is still a semidecidable problem having exponential time complexity. On the other hand swarm intelligence is a swiftly growing research area for solving optimization problems. This paper presents a novel approach for automated theorem proving using metaheuristics. In the present research we have tried to combine these two entirely different zones of computer science, i.e. meta-heuristics and concrete logic via modeling theorem provers as an optimization problem in a sound practical manner. Also we have experimentally shown how to automate first order reasoning using Artificial Bee Colony algorithm on a sample problem expressed in First-order predicate calculus.

Tri-Objective NSGA-II Based Methods for Load Balancing

“…The model was run on the benchmark [5] (consistent,inconsistent,semi − consistent(c,i,s)) and their corresponding subtypes (hihi,hilo,lohi,lolo), the functions converge towards an optimum in subsequent iterations. The simulation was run on an Intel Core − i5 processor, with 8 GB RAM.…”

“…As a result of the latter, the load on all machines would be balanced, and would reduce the chance of overload greatly. To prove that the model works, we run it on a standard data-set given in [5] for heterogeneous machines. The data-set has different conditions for values of resource utilization.…”