In this paper, we propose a Stochastic Dynamic Batch Local Search (SDBLS) algorithm to train Elman Neural Network (ENN) for Dynamic Systems Identification (DSI). First, we propose a new Batch Local Search (BLS) algorithm for ENN from a new angle instead of traditional Back Propagation (BP) based gradient descent technique, then add the stochastic dynamic signal into the network in order to avoi...

Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence optimization algorithms that works based on population and stochastic search. In order to achieve acceptable result, there are many parameters needs to be adjusted in AFSA. Among these parameters, visual and step are very significant in view of the fact that artificial fish basically move based on these parameters. In stand...

This paper proposes a generalized descent directions-guided multiobjective algorithm (DDMOA2). DDMOA2 uses the scalarizing fitness assignment in its parent and environmental selection procedures. The population consists of leader and non-leader individuals. Each individual in the population is represented by a tuple containing its genotype as well as the set of strategy parameters. The main nov...

This paper presents the steps followed in the design of hybrid stochastic local search algorithms for biobjective permutation flow shop scheduling problems. In particular, this paper tackles the three pairwise combinations of the objectives (i) makespan, (ii) the sum of the completion times of the jobs, and (iii) the weighted total tardiness of all jobs. The proposed algorithms are combinations...

This paper presents an Improved Memetic Algorithm (IMA) designed to compute near-optimal solutions for the antibandwidth problem. It incorporates two distinguishing features: an efficient heuristic to generate a good quality initial population and a local search operator based on a Stochastic Hill Climbing algorithm. The most suitable combination of parameter values for IMA is determined by emp...

Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a parameterized policy space in order to maximize the associated value function averaged over some predefined distribution. It is probably commonly believed that the best one can hope in general from such an approach is to get a local optimum of this criterion. In t...

An important practical problem in real world schedule execution is the occurrence of unforeseen events. When any changes in a production environment occur, the schedule has to be revised. Considering time consuming and shop floor nervousness, reactive repair of a disrupted schedule is a better alternative to total rescheduling. Most of the researchers have focused on heuristic based schedule re...

We describe LPSP, a domain-independent planning algorithm that searches the space of linear plans using stochastic local search techniques. Because linear plans, rather than propositional assignments, comprise the states of LPSP’s search space, we can incorporate into its search various operators that are suitable for manipulating plans, such as plan-step reordering based on action dependencies...

Augmented Downhill Simplex Method (ADSM) is introduced here, that is a heuristic combination of Downhill Simplex Method (DSM) with Random Search algorithm. In fact, DSM is an interpretable nonlinear local optimization method. However, it is a local exploitation algorithm; so, it can be trapped in a local minimum. In contrast, random search is a global exploration, but less efficient. Here, rand...

This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to ...