An efficient Tabu-search optimized regression for data-driven modeling

Abstract

In the past decade, data science became trendy and in-demand due to the necessity to capture, process, maintain, analyze and communicate data. Multiple regressions and artificial neural networks are both used for the analysis and handling of data. This work explores the use of meta-heuristic optimization to find optimal regression kernel for data fitting. It is shown that optimizing the regression kernel improve both the fitting and predictive ability of the regression. For instance, Tabu-search optimization is used to find the best least-squares regression kernel for different applications of buckling of straight columns and artificially generated data. Four independent parameters were used as input and a large pool of monomial search domain is initially considered. Different input parameters are also tested and the benefits of using of independent input parameters is shown.

Chaotic Honeybees Optimization Algorithms Approach for Traveling Salesperson Problem

“…e solution quality is in equation (7), which is the percentage error between the average value of the solutions found (PSE) for the pure MBO and the three chaotic variants with the best-known solutions (BKS) of the TSPLIB as shown in the following equation:…”

“…Efficient hybrid methods have also been proposed by combining these algorithms. Based on a single solution (direct search algorithms), the following algorithms can be found: simulate annealing (SA) [6], taboo search (TS) [7,8], random walk (RW) [9], and hill climbing (HC) [10], among others, and population-based algorithms such as spider monkey optimization (SMO) [11]; particle swarm optimization (PSO) [12][13][14]; ant colony optimization (ACO) [15]; artificial immune system (AIS) [16]; whale optimization [17]; genetic algorithm (GA) [18,19]; firefly algorithm [20]; grey wolf optimizer (GWO) [21]; bee algorithm (BA) [22]; artificial bee colony (ABC) [23]; queen bee evolution (QBE) [24]; bee system (BS) [25,26]; bee colonies optimization (BCO) [27]; BeeAdHoc [28,29]; and marriage in honey bees optimization (MBO) [30,31] and its different versions such as honey bees mating optimization (HBMO) [32,33] fast marriage in honey bees optimization (FMHBO) [34], and honey bees optimization (HBO) [35].…”

Increasing Efficiency in Inventory Control of Products with Sporadic Demand Using Simulation

“…In the case of sporadic demand, the literature talks about lumpiness (see, e.g., Kukreja & Schmidt, 2005;Lowas III & Ciarallo, 2016). A more efficient examination or reduction of search space could then be achieved by combining past stock movement simulation with metaheuristics such as tabu search (Ghnatios et al, 2019), simulated annealing (Yuan et al, 2022) or an evolutionary algorithm (Pasandideh et al, 2011). This represents the direction for further development of our solution and challenges for future work.…”

A nonparametric probabilistic method to enhance PGD solutions with data-driven approach, application to the automated tape placement process

“…On the other hand, with the current development of machine learning and data-driven techniques, fitting data using surrogate models based on regressions, neural network is becoming widely available and increasingly popular [14,24,32,47]. However, these surrogate models do not impose naturally physical conditions like the conservation of mass or energy.…”