This contribution presents a new memetic algorithm for continuous optimization problems, which is specially designed for applying intense local search methods. These local search methods make use of explicit strategy parameters to guide the search, and adapt these parameters with the purpose of producing more effective solutions. They may achieve accurate results, at the cost of requiring high ...

OF DISSERTATION EXAMINING THE ROLE OF LOCAL OPTIMA AND SCHEMA PROCESSING IN GENETIC SEARCH Several factors contribute to making search problems easy or di cult. One of these factors is the modality of the tness landscape. Quite often, when local search algorithms fail to locate the global optimum, it is because the algorithm converged to a local optimum. The majority of local search methods in ...

  We consider the maximal covering location-allocation problem with multiple servers. The objective is to maximize the population covered, subject to constraints on the number of service centers, total number of servers in all centers, and the average waiting time at each center. Each center operates as an M/M/k queuing system with variable number of servers. The total costs of establishing cen...

While routing in multi-hop packet radio networks (static Ad-hoc wireless networks), it is crucial to minimize power consumption since nodes are powered by batteries of limited capacity and it is expensive to recharge the device. This paper studies the problem of broadcast routing in radio networks. Given a network with an identified source node, any broadcast routing is considered as a directed...

This paper examines the factors affecting the quality of solution found by meta-heuristic search when optimising object-oriented software class models. From the algorithmic perspective, we examine the effect of encoding, choice of components such as the global search heuristic, and various means of incorporating problemand instance-specific information. We also consider the effect of problem ch...

The traveling salesman problem (TSP) is a well-known optimization problem in graph theory, as well as in operations research that has nowadays received much attention because of its practical applications in industrial and service problems. In this problem, a salesman starts to move from an arbitrary place called depot and after visits all of the nodes, finally comes back to the depot. The obje...

This paper studies the hybrid flow shop scheduling where the optimization criterion is the minimization of total tardiness. First, the problem is formulated as a mixed integer linear programming model. Then, to solve large problem sizes, an artificial immune algorithm hybridized with a simple local search in form of simulated annealing is proposed. Two experiments are carried out to evaluate th...

Algorithms based on local search are popular for solving many optimization problems including the maximum satisfiability problem (MAXSAT). With regard to MAXSAT, the state of the art in performance for universal (i.e. non specialized solvers) seems to be variants of Simulated Annealing (SA) and MaxWalkSat (MWS), stochastic local search methods. Local search methods are conceptually simple, and ...

gravitational search algorithm (gsa) is one of the newest swarm based optimization algorithms, which has been inspired by the newtonian laws of gravity and motion. gsa has empirically shown to be an efficient and robust stochastic search algorithm. since introducing gsa a convergence analysis of this algorithm has not yet been developed. this paper introduces the first attempt to a formal conve...