Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes an multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving on...

Ant Colony Optimization (ACO) a nature-inspired metaheuristic algorithm has been successfully applied in the traveling salesman problem (TSP) and a variety of combinatorial problems. In fact, ACO can effectively fit to discrete optimization problems and exploit pre-knowledge of the problems for a faster convergence. We present an improved version of ACO with a kind of Genetic semi-random-restar...

We propose a hybrid algorithm based on the Ant Colony Optimization (ACO) meta-heuristic, in conjunction with four well-known elimination rules, to tackle the NP -hard single-machine scheduling problem to minimize the total job tardiness. The hybrid algorithm has the same running time as that of ACO. We conducted extensive computational experiments to test the performance of the hybrid algorithm...

Bayesian Multi-net (BMN) classifiers consist of several local models, one for each data subset, to model asymmetric, more consistent dependency relationships among variables in each subset. This paper extends an earlier work of ours and proposes several contributions to the field of clustering-based BMN classifiers, using Ant Colony Optimization (ACO). First, we introduce a new medoidbased meth...

D-Aldohexopyranoside:cytochrome c oxidoreductase (ACO) was strongly induced by cellobiose, alpha-methylglucoside, beta-methylglucoside, kojibiose, and sophorose. Induction was rapid, and ACO was readily detectable within 10 min after addition of cellobiose as inducer. Although not measurable for 30 to 40 min after addition of inducer, once started, the rate of induction with alpha-methylglucosi...

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the AC...

Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is addressed. Usually, permutation-encoded DOPs, e.g., dynamic travelling salesman problems, are addressed using ACO algorithms whereas binary-encoded DOPs, e.g., dynamic knapsack problems, are tackled by evolutionary algorithms (EAs). This is because of the...

Two flavonoid-deficient mutants, designated as fldL-1 and fldL-2, were isolated in EMS-mutagenized (0.15%, 10 h) M(2) progeny of grass pea (Lathyrus sativus L.). Both the mutants contained total leaf flavonoid content only 20% of their mother varieties. Genetic analysis revealed monogenic recessive inheritance of the trait, controlled by two different nonallelic loci. The two mutants differed s...

This brief updates Brief No. 2014-3 and explains changes in the Centers for Medicare & Medicaid Services (CMS) Accountable Care Organization (ACO) regulations issued in June 2015 pertaining to beneficiary assignment for Medicare Shared Savings Program ACOs. Overall, the regulatory changes are intended to (1) encourage ACOs to participate in two-sided risk contracts, (2) increase the likelihood ...

—The implementation methods of the tasks assignment and tasks scheduling for Wireless Sensor and Actuator Network (WSAN) are proposed in this paper. Firstly, the distributed auction algorithm was used to assign tasks to the optimal actuators. Secondly, the Ant Colony Optimization (ACO) algorithm whose parameters were optimized by Particle Swarm Optimization algorithm (PSO) was proposed for the...