We present in this article new strategies for selecting nodes in interval Branch and Bound algorithms for constrained global optimization. The standard best-first strategy selects a node with the lowest lower bound of the objective estimate. We first propose new node selection policies where an upper bound of each node/box is also taken into account. The good accuracy of this upper bound achiev...

This paper proposes a hybrid ant colony optimization algorithm to solve the permutation flow-shop scheduling (PFS). The hybridization of ant colony optimization (ACO) with path relinking (PR), which combines the advantages of two individual algorithms, is the key innovative aspect of the approach. Path relinking (PR) can be interpreted as an evolutionary method where the high quality solutions ...

This paper presents iterated local search and great deluge trajectory metaheuristics for the linear ordering problem (LOP). Both metaheuristics are based on the TREE local search method introduced in Sakuraba and Yagiura, 2010 (Efficient local search algorithms for the linear ordering problem, International Transactions in Operational Research 17, pp. 711–737) that is the only method ever appli...

This paper proposes a novel hybrid t-way test generation strategy (where t indicates interaction strength), called High Level Hyper-Heuristic (HHH). HHH adopts Tabu Search as its high level meta-heuristic and leverages on the strength of four low level meta-heuristics, comprising of Teaching Learning based Optimization, Global Neighborhood Algorithm, Particle Swarm Optimization, and Cuckoo Sear...

With rapid advances of computing technologies and high speed networks, various high volume multimedia services have become popular in the Internet. Private Internet broadcasting is a typical way to support these services and overlay multicast technology is known to be a promising solution to support this method. In an overlay multicast network, members are dynamically joining or leaving their m...

The evolutionary approach called scatter search originated from strategies for creating composite decision rules and surrogate constraints. Recent studies demonstrate the practical advantages of this approach for solving a diverse array of optimisation problems from both classical and real world settings. Scatter search cont rasts with other evolutionary procedures, such as genetic algorithms, ...

This paper presents three proposals of multiobjective memetic algorithms to solve a more realistic extension of a classical industrial problem: time and space assembly line balancing. These three proposals are, respectively, based on evolutionary computation, ant colony optimisation, and greedy randomised search procedure. Different variants of these memetic algorithms have been developed and c...

This study outlines some weaknesses of existing Quantum-inspired Evolutionary Algorithms (QEA) by explaining how a bad choice of the rotation angle of qubit quantum gates can slow down optimal solutions discovery. A new algorithm, called Retroactive Quantum inspired Evolutionary Algorithm (rQEA), is proposed. With rQEA the rotation of individual’s amplitudes is performed by quantum gates accord...

The secular changes of height and body mass of girls and boys in the Bydgoszcz region in the period between 1935 and 1991 have been presented in this paper. Over the period of 56 years changes of the traits under investigation showed periodical diversification. Between 1935-1946 a regress was observed, between 1952 and 1971 the greatest average secular changes took place, the years from 1972 to...

This paper presents a genetic algorithm and a scatter search procedure to solve the well-known job shop scheduling problem. In contrast to the single population search performed by the genetic algorithm, the scatter search algorithm splits the population of solutions in a diverse and high-quality set to exchange information between individuals in a controlled way. Extensions from a single to a ...