Self-Tuning Lam Annealing: Learning Hyperparameters While Problem Solving

The runtime behavior of Simulated Annealing (SA), similar to other metaheuristics, is controlled by hyperparameters. For SA, hyperparameters affect how “temperature” varies over time, and “temperature” in turn affects SA’s decisions on whether or not to transition to neighboring states. It is typically necessary to tune the hyperparameters ahead of time. However, there are adaptive annealing schedules that use search feedback to evolve the “temperature” during the search. A classic and generally effective adaptive annealing schedule is the Modified Lam. Although effective, the Modified Lam can be sensitive to the scale of the cost function, and is sometimes slow to converge to its target behavior. In this paper, we present a novel variation of the Modified Lam that we call Self-Tuning Lam, which uses early search feedback to auto-adjust its self-adaptive behavior. Using a variety of discrete and continuous optimization problems, we demonstrate the ability of the Self-Tuning Lam to nearly instantaneously converge to its target behavior independent of the scale of the cost function, as well as its run length. Our implementation is integrated into Chips-n-Salsa, an open-source Java library for parallel and self-adaptive local search.

Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC), to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

Discrete Multistage Optimization and Hierarchical Market

New simple form of mixed solutions is described by bilinear continuous optimization processes. It enables investigate an analytic solutions and the connection between discrete and continuous optimization processes. Connection between discrete and continuous processes is stochastic. Discrete optimization processes are used for the control works in levels and groups of the hierarchical market. Equilibrium between local and global levels of works is investigated in hierarchical market.

Design Optimization of a Vehicle B-Pillar Subjected to Roof Crush Using Mixed Reactive Taboo Search

The primary obstacle in automated design for crashworthiness is the heavy computational resources required during the optimization processes. Hence it is desirable to develop efficient optimization algorithms capable of finding good solutions without requiring too many model simulations. This paper presents an efficient mixed discrete and continuous optimization algorithm, Mixed Reactive Taboo Search (MRTS), and its application to the design of a vehicle B-Pillar subjected to roof crush conditions. The problem is sophisticated enough to explore the MRTS’ capability of identifying multiple local optima with a single optimization run, yet the associated finite element model (FEM) is not too large to make the computational resources required for global optimization prohibitive. The optimization results demonstrated that a single run of MRTS identified a set of better designs with smaller number of simulation runs, than multiple runs of Sequential Quadratic Programming (SQP) with several starting points.