There are some questions concerning the applicability of meta-heuristic methods for real-world problems; further, researchers claim there is a growing gap between research and practice in this area. The reason that complexity problems very fast (e.g. due to globalisation), while experiment with benchmark fundamentally same as those 50 years ago. Thus need new class reflect characteristics problems. In paper, two main introduced: combination interdependence. We argue usually consist or more...

In this paper, we investigate three important properties (stability, local convergence, and transformation invariance) of a variant particle swarm optimization (PSO) called standard PSO 2011 (SPSO2011). Through some experiments, identify boundaries coefficients for algorithm that ensure particles converge to their equilibrium. Our experiments show these convergence are: 1) dependent on the number dimensions problem; 2) different from other variants; 3) not affected by stagnation assumption....

Real-world optimization problems often consist of several NP-hard that interact with each other. The goal this paper is to provide a benchmark suite promotes research the interaction between and their mutual influence. We establish comprehensive for traveling thief problem (TTP) which combines salesman knapsack problem. Our builds on common benchmarks two sub-problems grant basis examine potential hardness imposed by combining classical problems. Furthermore, we present some simple...

Many outstanding studies have reported promising results in seizure forecasting, one of the most challenging predictive data analysis problems. This is mainly because electroencephalogram (EEG) bio-signal intensity very small, $\mu \text{V}$ range, and there are significant sensing difficulties given physiological non-physiological artifacts. Today process accurate epileptic identification labeling done by neurologists. The current unpredictability activities together with lack reliable...

In this letter, we study the first- and second-order stabilities of a stochastic recurrence relation that represents class particle swarm optimization (PSO) algorithms. We assume personal global best vectors in are random variables (with arbitrary means variances) updated during run so our calculations do not require stagnation assumption. prove convergence expectation variance positions generated by is independent mean distribution vectors. also provide boundaries for compare them with...

In this paper, we investigate movement patterns of a particle in the swarm optimization (PSO) algorithm. We characterize by two factors: 1) correlation between its consecutive positions and 2) range movement. introduce base frequency as measure for variance determine theoretically show how they change with values coefficients. extract system equations that enables practitioners to find coefficients' guarantee achieving given movement, i.e., control pattern particles. also if is small, mid...

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods to be implemented in implanted devices. We present a novel method automatic detection based on iEEG data that outperforms current terms while maintaining the accuracy. The proposed algorithm incorporates an channel...

Outstanding seizure detection algorithms have been developed over past two decades. Despite this success, their implementations as part of implantable or wearable devices are still limited. These works mainly based on heavily handcrafted feature extraction, which is computationally expensive and shown to be data set specific. issues greatly limit the applicability such methods hardware implementation, including in-silicon application specific integrated circuits. In paper, we propose an...

Seizure prediction has attracted a growing attention as one of the most challenging predictive data analysis efforts in order to improve life patients living with drug-resistant epilepsy and tonic seizures. Many outstanding works have been reporting great results providing sensible indirect (warning systems) or direct (interactive neural-stimulation) control over refractory seizures, some which achieved high performance. However, many put heavily handcraft feature extraction and/or carefully...

Many real-world problems are composed of two or more that interdependent on each other. The interaction such usually is quite complex and solving problem separately cannot guarantee the optimal solution for overall multi-component problem. In this paper we experiment with one particular 2-component problem, namely Traveling Thief Problem (TTP). TTP Salesman (TSP) Knapsack (KP). We investigate heuristic methods to deal TTP. first approach decompose into sub-problems, solve them by separate...

In this paper, the underlying assumptions that have been used for designing adaptive particle swarm optimization (PSO) algorithms in past years are theoretically investigated. I relate these to movement patterns of particles controlled by coefficient values (inertia weight and acceleration coefficients) introduce three factors, namely autocorrelation positions, average distance each iteration, focus search, describe patterns. show how factors represent a within they affected coefficients...

Autonomous driving is one of the newly emerging feats in artificial intelligence (AI). The challenge developing autonomous cars to design controllers that can steer a vehicle right direction with enough speed. A good controller activates set multiple actuators simultaneously. output function sensory inputs. Nowadays, are mostly developed by connecting car simulator machine learning (ML) algorithm. provides pragmatic environment for simulated cars. ML algorithm, on other hand, does job an...

In this paper we propose a novel and efficient method for majority gate-based design. The basic Boolean primitive in quantum cellular automata (QCA) is the gate. Method reducing number of gates required computing functions developed to facilitate conversion sum products (SOP) expression into QCA logic. This based on genetic algorithm can reduce hardware requirements We will show that proposed approach very deriving simplified

Two approaches for solving numerical continuous domain constrained optimization problems are proposed and experimented with. The first approach is based on particle swarm algorithm with a new mutation operator in its velocity updating rule. Also, gradient incorporated into the algorithm. This uses ε-level constraint handling method. second covariance matrix adaptation evolutionary strategy same method constraints. It experimentally shown that needs less number of function evaluations than...

In a particle swarm optimization algorithm (PSO) it is essential to guarantee convergence of particles point in the search space (this property called stability particles). It also important that PSO converges local optimum property). Further, usually expected performance not affected by rotating rotation sensitivity). this paper, these three properties, i.e. particles, convergence, and sensitivity are investigated for variant Standard PSO2011 (SPSO2011). We experimentally define boundaries...