Suboptimal Choice Behaviour across Different Reinforcement Probabilities

Six adult roosters’ choice behaviour was investigated across a series of five experimental conditions and a series of replication of the same five experimental conditions. Stagner and Zentall (2010) found that pigeons prefer to choose an alternative with highly reliable discriminative stimuli but with less food reward over an alternative with non-discriminative stimuli but with more food reward. The current research systematically changed the probability of reinforcement associated with the discriminative stimulus through a series of experimental conditions. Experimental sessions were completed with six adult roosters. The experimental procedure was based on Stagner and Zentall’s (2010) experiment in which the suboptimal alternative with discriminative stimuli was associated with 100% reinforcement on 20% of the trials, and non-reinforcement on 80% of the trials; the optimal alternative with non-discriminative stimuli was associated with both 50% reinforcement on all trials. This research modified the probabilities of reinforcement associated with the discriminative alternative. In the first experimental condition, the probability of getting access to reinforcement was the same (50%) for each discriminative stimulus, thus, what was seen for the first time was that both alternatives were associated with non-discriminative stimuli. To insure reliability, a replication of the conditions was done after the first five experimental conditions were completed. The results showed that four of the roosters had suboptimal choice behaviour in the first five experimental conditions; however, only two of them maintained such suboptimal behaviour in the replication conditions. This result does not support the idea that the suboptimal choice behaviour with strong discriminative stimuli is a robust effect

Generalized Firefly Algorithm for optimal transmit beamforming

This paper proposes a generalized Firefly Algorithm (FA) to solve an optimization framework having objective function and constraints as multivariate functions of independent optimization variables. Four representative examples of how the proposed generalized FA can be adopted to solve downlink beamforming problems are shown for a classic transmit beamforming, cognitive beamforming, reconfigurable-intelligent-surfaces-aided (RIS-aided) transmit beamforming, and RIS-aided wireless power transfer (WPT). Complexity analyzes indicate that in large-antenna regimes the proposed FA approaches require less computational complexity than their corresponding interior point methods (IPMs) do, yet demand a higher complexity than the iterative and the successive convex approximation (SCA) approaches do. Simulation results reveal that the proposed FA attains the same global optimal solution as that of the IPM for an optimization problem in cognitive beamforming. On the other hand, the proposed FA approaches outperform the iterative, IPM and SCA in terms of obtaining better solution for optimization problems, respectively, for a classic transmit beamforming, RIS-aided transmit beamforming and RIS-aided WPT

Firefly algorithm for beamforming design in RIS-aided communication systems

This paper studies a non-convex power minimization problem for reconfigurable-intelligent-surfaces-aided communication systems whose constraints are multivariate functions of two independent optimization variables, i.e., active and passive beamforming vectors. A widely adopted alternative optimization (AO) approach approximates the originally non-convex problem by two convex sub-optimization problems where each sub-optimization problem deals with one variable considering the other variable as a constant. The solution for the original problem is obtained by iteratively solving these sub-optimization problems. Although the AO approach converts the original NP-hard optimization problem to two convex sub-problems, the solutions attained by this method may not be the global optimal solution due to the approximation process as well as the inherent non-convexity of the original problem. To overcome the issue, this paper adopts a nature-inspired optimization approach and introduces a novel Firefly algorithm (FA) to simultaneously solve for two independent optimization variables of the originally non- convex optimization problem. Computational complexity analyses are provided for the proposed FA and the AO approaches. Simulation results reveal that the proposed FA approach prevails its AO counterpart in obtaining a better solution for the under- studied optimization problem with the same order of computational complexity