Novel Metaheuristic Based on Iterated Constructive Stochastic Heuristic: Dhouib-Matrix-3 (DM3)

This paper presents a new metaheuristic named Dhouib-Matrix-3 (DM3) inspired by our recently developed constructive stochastic heuristic Dhouib-Matrix-TSP2 (DM-TSP2) and characterized by only one parameter: the number of iterations. The proposed metaheuristic DM3 is an iterative algorithm in which every iteration is based on two relay hybridization techniques. At first, the constructive stochastic heuristic DM-TSP2 starts by generating a different initial basic feasible solution and then each solution is intensified by the novel procedure Far-to-Near which exchanges far cities by closer ones using three perturbation techniques: insertion, exchange, and 2-opt. Experimental results carried out on the classical travelling salesman problem using the well-known TSP-LIB benchmark instances demonstrate that our approach DM3 outclasses the simulated annealing algorithm, the genetic algorithm, and the cellular genetic algorithm. Furthermore, the proposed DM3 is statistically concurrent to the hybrid simulated annealing cellular genetic algorithm. Nevertheless, DM3 is easier to implement and needs only one parameter to identify (the maximum number of iterations).

GPU-Based Hybrid Cellular Genetic Algorithm for Job-Shop Scheduling Problem

In task scheduling, the job-shop scheduling problem is notorious for being a combinatorial optimization problem; it is considered among the largest class of NP-hard problems. In this paper, a parallel implementation of hybrid cellular genetic algorithm is proposed in order to reach the best solutions at a minimum execution time. To avoid additional computation time and for real-time control, the fitness evaluation and genetic operations are entirely executed on a graphic processing unit in parallel; moreover, the chosen genetic representation, as well as the crossover, will always give a feasible solution. In this paper, a two-level scheme is proposed; the first and fastest uses several subpopulations in the same block, and the best solutions migrate between subpopulations. To achieve the optimal performance of the device and to reshape a more complex problem, a projection of the first on different blocks will make the second level. The proposed solution leads to speedups 18 times higher when compared to the best-performing algorithms.

A Hybrid Cellular Genetic Algorithm for the Traveling Salesman Problem

The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. Genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. However, it has some issues for solving TSP, including quickly falling into the local optimum and an insufficient optimization precision. To address TSP effectively, this paper proposes a hybrid Cellular Genetic Algorithm with Simulated Annealing (SA) Algorithm (SCGA). Firstly, SCGA is an improved Genetic Algorithm (GA) based on the Cellular Automata (CA). The selection operation in SCGA is performed according to the state of the cell. Secondly, SCGA, combined with SA, introduces an elitist strategy to improve the speed of the convergence. Finally, the proposed algorithm is tested against 13 standard benchmark instances from the TSPLIB to confirm the performance of the three cellular automata rules. The experimental results show that, in most instances, the results obtained by SCGA using rule 2 are better and more stable than the results of using rule 1 and rule 3. At the same time, we compared the experimental results with GA, SA, and Cellular Genetic Algorithm (CGA) to verify the performance of SCGA. The comparison results show that the distance obtained by the proposed algorithm is shortened by a mean of 7% compared with the other three algorithms, which is closer to the theoretical optimal value and has good robustness.

Large permeability metamaterial design optimization for low frequency applications

This paper presents a novel method for solving multi-objective optimization problems based on single-objective cellular genetic algorithm. In the proposed multi-objective cellular genetic algorithm, the objectives are divided into the primary objective and the secondary objective according to the preferences of a decision maker. The primary objective is used as the driving force for individual updating, while the secondary objective is employed as the bias force to select neighbors. The proposed approach has ensured that the secondary objective is also evolving in the optimal direction, as evidenced by the numerical results on both a mathematical test function and a prototype metamaterial unit as reported in this paper.

Design optimization of metamaterial units using a genetic algorithm based optimization methodology

Purpose The purpose of this paper is to develop a pertinent design optimization methodology for symmetric designs of a metamaterial (MM) unit. Design/methodology/approach A cell division mechanism is introduced and used to design a new selecting mechanism in the proposed algorithm, a non-dominated sorting cellular genetic algorithm (NSCGA). Findings The numerical results on solving standard multi-objective test functions and a prototype MM unit positively demonstrate the advantages of the proposed NSCGA. Originality/value A new NSGAII-based optimization algorithm, NSCGA, for multi-objective optimization designs of a MM unit is proposed.