Multi agent collaborative search algorithm with adaptive weights

Abstract

This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS-AW). MACS is a multi-agent memetic scheme for multi-objective optimization originally developed to mix local and population-based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub-problem the agent had to solve; (ii) the population-based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS-AW is compared against some state-of-art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS-AW is applied to the solution of two real-life optimization problems and compared against MACS2.1. It will be shown that MACS-AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS-AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS-AW and its predecessor obtain same results.