Population-based Metaheuristic Approach Research Articles

Optimal intelligent fuzzy TID controller for an uncertain level process with actuator and system faults: Population-based metaheuristic approach

For the Conical Tow-Tank Non-Interacting Level (CTTNL) prototype system, this article discusses the design of an unique optimal fractional order TID controller employing a type-1 fuzzy set. To generate a linearized model transfer function for the nonlinear CTTNL process, local linearization around the equilibrium state was performed. This work compares the performance of a fuzzy tilted-integral-derivative (FTID) controller for the CTTNL system to a traditional proportional–integral–derivative (PID) controller. The parameters of the FTID controller were optimized using the Chicken Search Optimization (CSO) algorithm in this study, and those values were used to create a PID controller. Thereafter, the FTID and PID controllers’ performance is compared. The investigation shows that the FTID controller beats the PID controller in terms of IAE, ISE, ITAE, and ITSE integral errors. The suggested controller’s fault-tolerant performance was also assessed using the fault recovery time (Frt) parameter. The FTID controller outperforms traditional PID controllers in terms of transient and steady-state performance. The fault tolerance capabilities of the FTID controller will be tested on the CTTNL system in the future, using additive and multiplicative failures. Further research will include a comprehensive examination of robustness in the context of model parameter uncertainties.

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment

In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance. Although clustering is a discrete problem, the computational intelligence techniques are more suitable for load balancing and minimizing energy consumption with different operating constraints. The literature reveals that the swarm intelligence-inspired computational approaches give excellent results among population-based meta-heuristic approaches because of their more remarkable exploration ability. Conversely, in this work, load-balanced clustering for sustainable WSNs is presented using improved gray wolf optimization (IGWO). In a smart city environment, the significant parameters of energy-efficient load-balanced clustering involve the network lifetime, dead cluster heads, dead gateways, dead sensor nodes, and energy consumption while ensuring information exchange and communication among the sensors and cluster heads. Therefore, based on the above parameters, the proposed IGWO is compared with the existing GWO and several other techniques. Moreover, the convergence characteristics of the proposed algorithm are demonstrated for an extensive network in a smart city environment, which consists of 500 sensors and 50 cluster heads deployed in an area of 500 × 500 m2, and it was found to be significantly improved.

Population-based Metaheuristics for the Dynamic Minimum Cost Hybrid Berth Allocation Problem

This study considers the Dynamic Minimum Cost Hybrid Berth Allocation Problem (DMCHBAP) with fixed handling times of vessels. The objective function to be minimized consists of three components: costs of positioning, waiting, and tardiness of completion for all vessels. A mathematical formulation of DMCHBAP, based on Mixed Integer Linear Programming (MILP), is proposed and used within the framework of commercial CPLEX 12.3 solver. As the speed of finding high-quality solutions is of crucial importance for an efficient and reliable decision support system in container terminal, two population-based metaheuristic approaches to DMCHBAP are proposed: combined Genetic Algorithm (cGA) and improvement-based Bee Colony Optimization (BCOi). Both cGA and BCOi are evaluated and compared against each other and against state-of-the-art solution methods for DMCHBAP on five sets of problem instances. The conducted computational experiments and statistical analysis indicate that population-based metaheuristic methods represent promising approaches for DMCHBAP and similar problems in maritime transportation.

Composition-tuned lithium aluminosilicate as a new humidity-sensing ceramic material with high sensitivity

Though numerous types of humidity sensors have been proposed to date, the continuous emergence of new applications requires relentless improvements in sensor performance and a constant pursuit of new sensing materials. Herein, the composition of lithium aluminosilicates (LiAlSi2O6 and Li2Al2SiO6) that slightly absorb H2O molecules is tuned along with the addition of LiCl and Si2N3, in order to maximize the H2O adsorption. The optimal composition is discovered via use of a particle swarm optimization algorithm, which is a population-based metaheuristic approach. After three generations of searching, we finally develop Li2.10Al1.23Si1.54Cl0.05O5.95 (LAS-CNO), which shows the highest value of RRH=43 %/RRH=75 % (R is ionic resistance and RH is the relative humidity). Density-functional-theory calculation also reveals that the presence of Cl− and a vacancy defect on Al/Si sites contribute to preferential H2O adsorption on the (001) surface of LAS-CNO (hexagonal P6222). Accordingly, the magnitude of impedance (│Z│) at 100 Hz significantly varies from 200 MΩ (RH = ca. 0 %) to 1.0 kΩ (RH = 97 %), in contrast to relatively small decreases in │Z│ to 630 and 59 kΩ for LiAlSi2O6 and Li2Al2SiO6, respectively. An almost linear of │Z│ vs. RHs is also observed for RH values ranging between 9 and 97 %. Furthermore, the linearity is not weakened and the values of │Z│ are not significantly altered with measuring-frequencies. Due to strong H2O adsorption on LAS-CNO, hysteresis is noticeable between the adsorption and desorption processes, but other superior properties (sensitivity, stability, and linearity) validate that LAS-CNO could be a new humidity-sensing material.

Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL Ensemble

A textile multi-band antenna for LTE and 5G communication services, composed by a rectangular microstrip patch, two concentric annular slots and a U-Shaped slot, is considered in this paper. In the ground plane, three sleeved meanders have been introduced to modify the surface current distribution, leading to a bandwidth improvement. The U-Shaped slot, the dual circular slots, and the meanders shape have been optimized by means of the Coral Reefs Optimization with Substrate Layer algorithm (CRO-SL). This population-based meta-heuristic approach is a kind of ensemble algorithm for optimization (multi-method), in which different search operators are considered within the algorithm. We show that the CRO-SL is able to obtain a robust multi-band textile antenna, including LTE and 5G frequency bands. For the optimization process, the CRO-SL is guided by means of a fitness function obtained after the antenna simulation by a specific simulation software for electromagnetic analysis in the high frequency range.

Constructing population of initial university timetable: design and analysis

The construction of population of initial timetable is an essential stage in population-based metaheuristic approach for solving curriculum-based university course timetabling problem because it may impact the quality of the final timetable. This paper presents population of initial timetable construction approach in curriculum based course timetabling problem by using the graph heuristics to determine the sequential order of courses/lectures to be assigned in the timetable. The graph heuristics were implemented as single and combination of two heuristics. The courses in curriculum-based university course timetabling problem that was organized based on the heuristics setting will be repeatedly assigned to valid empty slots while fulfilling all the hard constraints. If a course is unable to be assigned to whichever slots because of no more valid empty slots, it will be inserted into the unscheduled courses/lectures list. The unscheduled courses/lectures list will be assigned later to the timetable using several procedures executed in a sequence. The approaches were tested on the ITC2007 instances and the results were analyzed with some statistical tests to determine the best setting of heuristics in the construction approach. The result shows that the construction approach with combination of largest degree followed by saturation degree heuristic, generate the maximum number of population of initial timetables. The result from this study can be used in the improvement stage of metaheuristic algorithm that uses population-based approach.

A population-based meta-heuristic approach for robust micro-geometry optimization of tooth profile in spur gears considering manufacturing uncertainties

The paper proposes a population-based meta-heuristic approach for the multi-objective robust optimization of tooth profiles, aimed at finding a set of micro-geometry modification parameters that allow to improve mechanical performance of spur gears. With the aim of making the optimization results reliable in real-life applications, a robust formulation of the optimization problem is generated by incorporating noise parameters that account for the influence of manufacturing uncertainties on the objective function. The described multi-objective gear optimization strategy is based on response surface (surrogate) models, allowing for checking the performance of a large number of candidate solutions in very short computational times. The computation efficiency of the proposed approach is the key that enables a simultaneous assessment of both linear and parabolic profile modifications, so that the most appropriate tooth geometry can be selected for the specific application. The proposed approach was successfully employed in a case study in which static transmission error and contact stress of a gear pair loaded by different torques were optimized under fatigue-related constraints and in presence of geometric variability due to manufacturing uncertainties.

Multiple Exponential Recombination for Differential Evolution.

Differential evolution (DE) is a popular population-based metaheuristic approach for solving numerical optimization problems. In recent years, considerable research has been devoted to the development of new mutation strategies and parameter adaptation mechanisms. However, as one of the basic algorithmic components of DE, the crossover operation has not been sufficiently examined in existing works. Most of the main DE variants solely employ traditional binomial recombination, which has intrinsic limitations in handling dependent subsets of variables. To fill this research niche, we propose a multiple exponential recombination that inherits all the main advantages of existing crossover operators while possessing a stronger ability in managing dependent variables. Multiple segments of the involved solutions will be exchanged during the proposed operator. The properties of the new scheme are examined both theoretically and empirically. Experimental results demonstrate the robustness of the proposed operator in solving problems with unknown variable interrelations.

Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory

Fuzzy Population-Based Meta-Heuristic Approaches for Attribute Reduction in Rough Set Theory

A comparative study of BA, APSO, GSA, hybrid PSOGSA and SPSO in dual channel speech enhancement

The present paper focuses on the suppression of background noise in speech signal by utilizing a powerful heuristic optimization algorithm called Bat algorithm (BA) for adaptive filtering in dual channel enhancement systems. Bat algorithm is a recently developed population based meta heuristic approach which is inspired by the hunting behavior of the bats. It is developed by Yang (2010). As a novel feature Bat algorithm is based on the echo location behavior of micro bats. BA uses the frequency tuning technique to increase the diversity of the solutions in the population, while at the same time it uses the automatic zooming and tries to balance the exploration and exploitation. A few studies have been carried out on the use of heuristics for ANC in speech enhancement by using standard particle swarm optimization (SPSO) and some of its variants till 2010. In order to extend these heuristic approaches to speech, accelerated particle swarm optimization (APSO) based enhancement approach has been proposed (Prajna et al. in Int J Speech Technol 17(4):341---351, 2014a; Prajna et al. in IJISA 6(4):1---10, 2014b, doi:10.5815/ijisa.2014.04.01; Prajna et al. in IEEE international conference on communications and signal processing (ICCSP), April 2014, pp 1457---1461, 2014c, doi:10.1109/ICCSP.2014.6950090). To overcome the problem of poor exploitation ability of APSO, another approach is proposed based on gravitational search algorithm (GSA) (Prajna et al. 2014a, b, c). To combine both the abilities of PSO and GSA algorithms, Hybrid PSOGSA algorithm is also proposed to ANC (Prajna et al. 2014a, b, c). To further improve the efficiency of adaptive filtering in ANC, by providing a dynamic balance between exploration and exploitation, BA is proposed to speech enhancement. This paper intends to present the Bat algorithm as an improved approach to ANC in speech enhancement when compared with that of SPSO, APSO, GSA and Hybrid PSOGSA based speech enhancement algorithms. The performance of all the algorithms is evaluated by computing four objective measures SNRI, PESQ, FAI and WSS, in two real world noise conditions Babble and Factory, at three different input SNR levels set at ?10, 0 and 5 dB. Simulation results prove that BA is the most successful algorithm of all the algorithms studied in this work, to suppress the background noise more effectively.

Distribution System Network Reconfiguration by using Artificial Bee Colony Algorithm.

This paper presents a new methodology using artificial bee's colony algorithm (ABC) in the radial distribution systems for the placement and size of Distributed Generators (DG). The ABC algorithm is a new population based meta heuristic approach exalted by intelligent foraging behavior of honey bee swarm. This paper presents a feeder reconfiguration problem with Distributed Generation to the distribution system. In the recent years the application of Distributed Generation (DG) has found increasing attention. Optimal reconfiguration needs selection of the best set of branches to be opened so that the resulting radial distribution system has improved voltage profile system and minimized power loss. The developed methodology is demonstrated by a radial distribution system with distributed generation. The study results show that the optimal on/off patterns of the switches can be identified which give the minimum power loss while respecting all the constraints.

Siting and Sizing of DG for Loss Reduction and Voltage Sag Mitigation in RDS Using ABC Algorithm

In order to reduce the power loss and to improve the voltage profile in the distribution system, distributed generators (DGs) are connected to load bus. To reduce the total power loss in the system, the most important process is to identify the proper location for fixing and sizing of DGs. This paper presents a new methodology using a new population based meta heuristic approach namely Artificial Bee Colony algorithm (ABC) for the placement of Distributed Generators (DG) in the radial distribution systems to reduce the real power loss, to improve the voltage profile & voltage sag mitigation. While these power loss reduction, voltage profile improvement and voltage sag mitigation has significant role in lessoning imposed expenditures to utility companies. The power loss reduction is important factor for utility companies because it is directly proportional to the company benefits in a competitive electricity market, while reaching the better power quality standards is too important as it has vital effect on customer orientation. In this paper an ABC algorithm is developed to gain these goals all together. In order to evaluate sag mitigation capability of the proposed algorithm, voltage in voltage sensitive buses is investigated. An existing 20KV network (32-bus system) has been chosen as test network and results are compared with the proposed method in the radial distribution system. DOI: http://dx.doi.org/10.11591/ijece.v3i6.4399

A comparative analysis of three metaheuristic methods applied to fuzzy cognitive maps learning

This work analyses the performance of three different population-based metaheuristic approaches applied to Fuzzy cognitive maps (FCM) learning in qualitative control of processes. Fuzzy cognitive maps permit to include the previous specialist knowledge in the control rule. Particularly, Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) are considered for obtaining appropriate weight matrices for learning the FCM. A statistical convergence analysis within 10000 simulations of each algorithm is presented. In order to validate the proposed approach, two industrial control process problems previously described in the literature are considered in this work.

Multiobjective Optimization for Optimal Placement and Size of DG using Shuffled Frog Leaping Algorithm

There has been a great interest in integration of distributed generation (DG) units at distribution level in the recent years. DGs can provide cost-effective, environmental friendly, higher power quality and more reliable energy solutions than conventional generation. For maximum power loss reduction, proper sizing and position of distributed generators are ardently necessary. This paper presents a simple method for optimizing cost and optimal placement of generators. A simple vector based load flow technique is implemented on 38 bus distribution systems. This paper presents a new methodology using a new population based meta heuristic approach namely Shuffled frog leaping algorithm for the placement of Distributed Generators (DG) in the radial distribution systems to reduce the real power losses and cost of the DG. The paper also focuses on optimization of weighting factor, which balances the cost and the loss factors and helps to build up desired objectives with maximum potential benefit. The proposed method has outperformed the other methods in terms of the quality of solution and computational efficiency.