A Memetic Evolutionary Algorithm-Based Optimization for Competitive Bid Data Analysis

Abstract

One of the most difficult decision-making problems for buyers is to identify which suppliers to provide contracts to of the biddable items for a competitive event after considering several conditions. This is an example of pure integer linear programming (ILP) problem with cost minimization where all the decision variables, i.e., the quantity of the biddable items to be awarded to suppliers, are always nonnegative integers. Normally for solving ILP, Gomory cutting plane or Branch and Bound technique using the simplex method are applied. But when the problem to be solved is highly constrained and a large number of variables are involved, finding a feasible solution is difficult and that can result in poor performance by these techniques. To address this, an improved memetic meta-heuristic evolutionary algorithm (EA) such as shuffled frog leaping algorithm (SFLA) is utilized to find the optimum solution satisfying all the constraints. The SFLA is a random population-based optimization technique inspired by natural memetics. It performs particle swarm optimization (PSO) like positional improvement in the local search and globally, it employs effective mixing of information using the shuffled complex evolution technique. In this paper, a modified shuffled frog leaping algorithm (MSFLA) is proposed where modification of SFLA is achieved by introducing supplier weightage and supplier acceptability to improve the quality of the solution with a more stable outcome. Simulation results and comparative study on highly constrained and a large number of items and suppliers’ instances from bidding data demonstrate the efficiency of the proposed hybrid meta-heuristic algorithm.