Abstract Variational quantum algorithms, a class of heuristics, are promising candidates for the demonstration useful computation. Finding best way to amplify performance these methods on hardware is an important task. Here, we evaluate optimization heuristics with existing techniques called “meta-learners.” We compare meta-learner evolutionary strategies, L-BFGS-B and Nelder-Mead approaches, t...

The economical determination of lot size with capacity constraints is a frequently complex, problem in the real world. In this paper, a multi-level problem of lotsizing with capacity constraints in a finite planning horizon is investigated. A combination of ant colony algorithm and a heuristic method called shifting technique is proposed for solving the problem. The parameters, including the co...

The primary goal of the research reported in this paper is to identify what criteria are responsible for the good performance of a heuristic rule evaluation function in a greedy topdown covering algorithm. We first argue that search heuristics for inductive rule learning algorithms typically trade off consistency and coverage, and we investigate this trade-off by determining optimal parameter s...

Instruction scheduling is a fundamental step for a mapping an application to a computational device. It takes a behavioral application specification and produces a schedule for the instructions onto a collection of system processing units. The objective is to minimize the completion time of the given application while effectively utilizing the computational resources. The instruction scheduling...

This paper investigates a comparative study which addresses the P/prec/Cmax scheduling problem, a notable NP-hard benchmark. MLP_SACS, a modified ant colony algorithm, is used to solve it. Its application provides us a better job allocation to machines. In front of each machine, the jobs are performed with three priority rules, the longest path (LP), a modified longest path (MLP) and a maximum ...

Meta-heuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of meta-heuristics. This primarily focuses on the significant progress which general frames within the meta-heuristics field ha...

The problem of finding good mappings is central to designing and executing applications efficiently in embedded systems. In heterogeneous multicores, which are ubiquitous today, this yields an intractably large design space possible mappings. Most methods explore using heuristics, many implicitly use geometric notions paper we the geometry mapping explicitly, for embeddings that capture its str...