Bio-inspired Computation

The paper describes a novel algorithm, inspired by the phenomenon of wisdom of crowds, for solving instances of NP-hard problems. The proposed approach achieves superior performance compared to the genetic algorithm-based approach and requires modest computational resources. On average, a 6%-9% improvement in quality of solutions has been observed.

ABSTRACT Cuckoo Search (CS) is one of the most recent population-based metaheuristics. CS algorithm is based on the cuckoo’s behavior and the mechanism of Lévy flights. Unfortunately, the standard CS algorithm is proposed only for continuous optimization problems. In this paper, we propose a discrete Binary Cuckoo Search algorithm (BCS) in order to deal with binary optimization problems. To get binary solutions, we have used a sigmoid function similar to that used in the binary particle swarm optimization algorithm. Computational results on some knapsack problem instances and Multidimensional knapsack problem instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

In molecular beam epitaxy, it is known that a planar surface may suffer from a morphological instability in favour to different front pattern formations. In this context, many studies turned their focus to the theoretical and numerical analysis of highly nonlinear partial differential equations which exhibit different scenarios ranging from spatio-temporal chaos to coarsening processes (i.e., an emerging pattern whose typical length scale with time). In this work our attention is addressed toward the study of a highly nonlinear front evolution equation proposed by Csahok et al. (1999) where the unknowns are the periodic steady states which play a major role in investigating the coarsening dynamics. Therefore the classical methods of Newton or a fixed point type are not suitable in this situation. To overcome this problem, we develop in this paper a new approach based on heuristic methods such as genetic algorithms in order to compute the unknowns.

This paper proposes a study of different dynamic objectives aggregation methods (DOAMs) in the context of a multi-objective evolutionary approach to portfolio optimisation. Since the incorporation of chaotic rules or behaviour in population-based optimisation algorithms has been shown to possibly enhance their searching ability, this study considers and evaluates also some chaotic rules in the dynamic weights generation process. The ability of the DOAMs to solve the portfolio rebalancing problem is investigated conducting a computational study on a set of instances based on real data. The portfolio model considers a set of realistic constraints and entails the simultaneous optimisation of the risk on portfolio, the expected return and the transaction cost.

7 This paper elucidates the computation of optimal controls for steel annealing processes as hybrid systems which comprise of one or more furnaces integrated with plant-wide planning and scheduling operations. A class of hybrid system is considered to capture the trade-off between metallurgical quality requirement and timely product delivery. Various optimization algorithms including particle swarm optimization algorithm (PSO) with time varying inertia weight methods, PSO with globally and locally tuned parameters (GLBest PSO), parameter free PSO (pf-PSO) and PSO like algorithm via extrapolation (ePSO), real coded genetic algorithm (RCGA) and two-phase hybrid real coded genetic algorithm (HRCGA) are considered to solve the optimal control problems for the steel annealing processes (SAP). The optimal solutions including optimal line speed, optimal cost, and job completion time and convergence rate obtained through all these optimization algorithms are compared with each other and also those obtained via the existing method, forward algorithm (FA). Various statistical analyses and analysis of variance (ANOVA) test and hypothesis t-test are carried out in order to compare the performance of each method in solving the optimal control problems of SAP. The comparative study of the performance of the various algorithms indicates that the PSO like algorithms, pf-PSO and ePSO are equally good and are also better than all the other optimization methods considered in this chapter.

ABSTRACT Choice design building based on D-error minimisation can be accomplished either by using predefined β values or by assuming probabilistic distributions for them. Several mathematical techniques have been used for both approaches in the past, resulting in algorithms that obtain efficient designs, which guarantee the high quality of the information that will be provided by the respondents. This paper proposes the use of a genetic algorithm for dealing with the problem of building designs with minimum D-error, describing the technique and applying it successfully to several benchmark cases. Design matrices, D-error values, percentages of level overlap and computation times are provided for each case.

The paper describes a novel algorithm, inspired by the phenomenon of wisdom of crowds, for solving instances of NP-hard problems. The proposed approach achieves superior performance compared to the genetic algorithm-based approach and requires modest computational resources. On average, a 6%-9% improvement in quality of solutions has been observed.