A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA

Abstract

This article describes a simulated annealing based multi-objective optimization algorithm that incorporates the concept of archive in order to provide a set of trade-off solutions of the problem under consideration. To determine the acceptance probability of a new solution visa -vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with two other existing and well-known multi-objective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulties. In particular, the proposed algorithm is found to be significantly superior for many-objective test problems (e.g., 4, 5, 10 and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm where the performance of the former is found to be generally superior to that of the latter.

Multi-objective multicast routing optimization in Cognitive Radio Networks

“…Therefore, we adopt two other metaheuristic algorithms, which have better features than EAs in solving our problems. The two algorithms are MOACS [1] and AMOSA [2]. For the rest of this section, Subsection A introduces the algorithms for generating a random multicast tree solution.…”

“…AMOSA is an algorithm proposed in reference [2] to use simulated annealing to solve multi-objective optimization problems. We adopt the code provided by reference [2] and modify the process of constructing a random multicast tree solution as introduced in previous subsections.…”

“…We adopt the code provided by reference [2] and modify the process of constructing a random multicast tree solution as introduced in previous subsections. AMOSA will return an archive which records all optimal multicast routing trees that can be found within a given number of iterations.…”

“…Given a multicast session of the MMHG, our goal is to find the multicast routing trees that minimize the worst case end-to-end delay (delay), maximize the multicast rate (rate) and minimize the number of transmission links (numOfLinks) used in the multicast tree. We apply two metaheuristic algorithms (Multi-Objective Ant Colony System optimization algorithm (MOACS) [1] and A Simulated Annealing-Based Multi-objective Optimization Algorithm (AMOSA) [2]) in solving the problem. We also study the scheduling problem of multicast routing trees obtained using the MMHG model.…”

PPCOF: A Robust Proportion-Preserving Composite Objective Function for Scale-Invariant Multi-Objective Optimization

“…Numerous industrial applications and analytical simulative studies require dealing with nonlinear programming intended to solve Multi-Objective Optimization Problems (MOOP) due to the signi cant roles they play in making decisions leading to fair settlements between con icting criteria taken into account in the design, planning, and control stages [1][2][3][4][5]. More clearly, practical applications are usually aimed at achieving more than one goal simultaneously, where satisfying each objective to the greatest extent demands ruining the performance or desirability in terms of others.…”