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Combinational logic circuits (CLCs) are basic building blocks of a system and optimization of these circuits in terms of reduced gates, transistors, or levels will lead to reduced area on chip, reduced power, and improved speed. Simulated annealing (SA) is a thermo-inspired metaheuristic used for solving various engineering and non-engineering problems. SA is also used for the realization and optimization of CLCs. Circuits with a large number of inputs and outputs require more generations for realization. Realization of the optimal circuit with fewer generations is desired as realization time increases with increase in the number of generations. In this paper, an attempt is made to realize circuits using population-based SA with fewer generations. SA with partitioning approach is proposed in this paper for circuits that could not be realized with fewer preset generations. To evaluate the performance of the proposed work, benchmark circuits from LGSynth'91 are considered, and it is observed that the success rate improved and realization time reduced with the proposed partitioning approach. During the evaluation, it is also observed that the gate count was reduced by 2.5–77.39% and the transistor count was reduced by 7.69–95.53% on using proposed work with fewer generations over circuits reported in the literature. [ABSTRACT FROM AUTHOR]

Many optimization problems in engineering and industrial design applications can be formulated as optimization problems with highly nonlinear objectives, subject to multiple complex constraints. Solving such optimization problems requires sophisticated algorithms and optimization techniques. A major trend in recent years is the use of nature-inspired metaheustic algorithms (NIMA). Despite the popularity of nature-inspired metaheuristic algorithms, there are still some challenging issues and open problems to be resolved. Two main issues related to current NIMAs are: there are over 540 algorithms in the literature, and there is no unified framework to understand the search mechanisms of different algorithms. Therefore, this paper attempts to analyse some similarities and differences among different algorithms and then presents a generalized evolutionary metaheuristic (GEM) in an attempt to unify some of the existing algorithms. After a brief discussion of some insights into nature-inspired algorithms and some open problems, we propose a generalized evolutionary metaheuristic algorithm to unify more than 20 different algorithms so as to understand their main steps and search mechanisms. We then test the unified GEM using 15 test benchmarks to validate its performance. Finally, further research topics are briefly discussed. [ABSTRACT FROM AUTHOR]

In recent years, many studies have been used in feed-forward neural network (FNN) to develop decision-making systems. The primary objective is to get the least error by finding the best combination of control parameters. It has been observed that FNNs using meta-heuristics techniques always converges very quickly towards the optimal positions but suffers from slow searching speeds at later stages of generation. Due to slow convergence, it is a prevalent phenomenon that traditional optimization does not ensure to find global optima. As a result, it falls under local optima. Recently, another meta-heuristic optimization-based algorithm called sine cosine algorithms (SCA) was introduced to solve the aforementioned issues. The algorithm is fundamentally predicated on two trigonometric functions, one being sine and the other being cosine. However, like other traditional approaches, SCA has a tendency to be stuck in sub-optimal regions due to poor exploration and exploitation capabilities. This paper proposes an improved version of SCA named chaotic oppositional SCA (COSCA) by integrating with chaos theory and oppositional based learning into the SCA optimization process. It is an incipient training method employed to train an FNN. Three benchmark problems are used to examine the precision and performance of FNNs equipped with COSCA, COPSO, OSCA, SCA, PSO, and backpropagation. The experimental results showed that, relative to other meta-heuristic optimization techniques, the COSCA technique is able to improve performance. [ABSTRACT FROM AUTHOR]

Reconfiguration of PV array has emerged as a promising solution to improve the power under partial shading conditions (PSC). The three main reconfiguration techniques are the physical array relocation method, electrical array reconfiguration (EAR), and the switching matrix generation based on optimization. The physical relocation techniques are complicated as they require hard labor. In the case of EAR, the optimal design of the switching matrix is still challenging. Therefore, to overcome these issues, this paper proposes a recent metaheuristic technique of Adaptive-JAYA optimization for the optimum reconfiguration of the PV array. Adaptive-JAYA is chosen for its simplicity and reliability, which makes it consume less memory, reducing the burden on processors. The proposed approaches are applied on a 9 × 9 PV array under eight shading patterns and also on a 9 × 15 PV array to analyze the competency of the proposed approach on unsymmetrical shading. The MATLAB results are quantitatively analyzed, and a comprehensive comparison is performed with many existing reconfiguration techniques. It is proved that the proposed Adaptive-JAYA approach improves the output power by 26.12%, 22.20%, 7.61%, and 7.68% compared to the TCT for the four shading patterns, i.e. SW, LW, SN, and LN. [ABSTRACT FROM AUTHOR]

One of the salient features of real-world networks such as social networks is the existence of community structures. Because of the importance of groups and communities in social networks, various algorithms have been proposed to identify communities in this type of dynamic networks. In this paper, we present a new approach to community recognition in dynamic social networks, which is multi-objective and metaheuristic. Our approach is to improve the Grey Wolf Optimizer algorithm and the Label Propagation algorithm and to combine the two algorithms for better performance. We performed our experiments on two artifi- cial and real datasets, and the results show that our proposed method performs better compared to present algorithms in terms of both quality and detection speed. We also applied our proposed algorithm to 23 base functions, which performed better than the other metaheuristic algorithms. At the end, the performance of our proposed algorithm is compared to six other clustering methods on nine datasets from the UCI machine learning laboratory. The simulation results show the effectiveness of the proposed algorithm for solving data clustering problems. [ABSTRACT FROM AUTHOR]

The optimization problems are becoming more complicated, requiring new and efficient optimization techniques to solve them. Many bio-inspired meta-heuristic algorithms have emerged in the last decade to solve these complex problems as most of these algorithms may be trapped into local optima and could not effectively solve all types of optimization problems. Hence, researchers are still trying to develop new and better optimization algorithms. This paper introduces a novel biologically-based optimization algorithm called circulatory system-based optimization (CSBO). CSBO is modeled based on the function of the body's blood vessels with two distinctive circuits, i.e. pulmonary and systemic circuits. The proposed CSBO algorithm is tested on a wide variety of complex functions of the real world and validated with the standard meta-heuristic algorithms. The results indicate that the CSBO algorithm successfully achieves the optimal solutions and avoids local optima. Note that the source code of the CSBO algorithm is publicly available at . [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic algorithm called Thieves and Police Algorithm (TPA), for solving probabilistic optimization problems. The proposed algorithm is designed in five different phases each following a certain objective. In this algorithm, conventional matters between the thieves' team and the police are inspired as a social phenomenon. Motivation of thieves for stealing more properties is utilized in this algorithm to seek the optimum solution, and police track that result in fear and no risk taking of the thieves to enter areas under police surveillance is inspired to omit areas with weaker solutions. Algorithm optimization procedure ends with apprehending the leader thief by police as the optimum solution. The performance of the TPA is evaluated in terms of local optima avoidance, exploration, exploitation, effectiveness, and convergence properties using a set of classical and modern test functions. Comparing the outcomes of other meta-heuristic optimization algorithms, the successful performance of the TPA in solving different optimization problems is confirmed. Moreover, the efficiency of the TPA in terms of solving problems with probabilistic nature is assessed by solving an optimization problem in power engineering area, known as Probabilistic Power Flow (PPF), in a Micro-Grid with renewable energy resources. Based on the outcomes, the proficiency of the proposed algorithm is confirmed by solving the PPF problem and producing timely acceptable results. Hence, the TPA algorithm is suggested to solve complicated problems, with probabilistic nature, in the optimization field and other optimization problems. [ABSTRACT FROM AUTHOR]

This paper aims to provide a novel approach for improving urban land cover classification accuracy, which combines the Elephant Herding Optimization (EHO) algorithm as a meta-heuristic optimization method with the Random Forest (RF) classifier. The proposed approach involves both classifier hyperparameter tuning and feature selection for a data set of a selected urban area. The EHO and the RF algorithms were both utilized in a hybrid system (EHO-RF) to find an optimal classification model with a tuned set of hyperparameters and features (predictor variables). EHO-RF model tuning was followed by backward feature elimination using RF, which further reduced data dimensionality. This work also combines two well-known optimization and feature selection algorithms to enhance accuracy assessment of the proposed approach. Grid search optimization was combined with Variable Selection Using Random Forests (VSURF) algorithm to build an optimization system (Grid-VSURF) that is similar to the workflow of EHO-RF. Objective functions for both Grid-VSURF and EHO-RF were designed to perform 10-fold cross-validation to reduce over-fitting. An area in Deerfield Beach city, Florida, USA, was selected as the study area representing an urban land cover. The testing dataset used in this study represents a performance benchmarking dataset, which has been utilized in many studies. EHO-RF results showed a significant improvement with an overall map accuracy of 83.83%, compared with Grid-VSURF results with an overall map accuracy of 74.75%. The number of input features was significantly reduced by both approaches with 82.30% reduction using EHO-RF with backward elimination and 89.70% reduction using Grid-VSURF. [ABSTRACT FROM AUTHOR]

Salp Swarm Algorithm (SSA) is a novel swarm intelligent algorithm with good performance. However, like other swarm-based algorithms, it has insufficiencies of low convergence precision and slow convergence speed when dealing with high-dimensional complex optimisation problems. In response to this concerning issue, in this paper, we propose an improved SSA named as WASSA. First of all, dynamic weight factor is added to the update formula of population position, aiming to balance global exploration and local exploitation. In addition, in order to avoid premature convergence and evolution stagnation, an adaptive mutation strategy is introduced during the evolution process. Disturbance to the global extremum promotes the population to jump out of local extremum and continue to search for an optimal solution. The experiments conducted on a set of 28 benchmark functions show that the improved algorithm presented in this paper displays obvious superiority in convergence performance, robustness as well as the ability to escape local optimum when compared with SSA. [ABSTRACT FROM AUTHOR]

Oscillatory instability in modern power system has increased due to increasing complexity and integration of dynamic loads. Stability analysis of such interconnected power system is very prominent in the presence of uncertain load/generation. In this paper, a power system stabilizer (PSS) design approach, which aims at enhancing the oscillatory stability of the multi-machine power system over the specified uncertainty range in forecasted load/generation, is presented. With non-statistical uncertainty, problem of selecting design parameters of the PSS is formulated as an optimization problem with minimization of eigenvalues and damping ratios based multi-objective function. In order to account the non-statistical uncertainties, a boundary active power loss (BAPL) based objective function is proposed. This non-linear BAPL objective function is minimized for determining the optimal setting of the generators voltage and taps of the online tap changing transformers (OLTC) under various power system constraints. In this paper, both the objective functions are solved by a new metaheuristic technique known as gray wolf optimization (GWO). Boundary value-based approach is used to minimize the repeated load flows under uncertain load/generation scenarios. Improved small-signal stability (SSS) is achieved with optimal active power loss of uncertain power system. Eigenvalue and time domain analysis for New England system are carried out under wide range of disturbances to demonstrate the potential of the proposed approach. [ABSTRACT FROM AUTHOR]

During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. Evolutionary intelligence is a research field that models the behaviour of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complicated optimizable systems. In this paper, a novel evolutionary technique called Artificial Spider Algorithm (ASA) for solving optimization tasks in unconstrained problems with high nonlinearity is proposed. The ASA is based on the simulation of spider behaviour. For this purpose, a new metaphysical method according to spinning web and hunting insects via spider is inspired in nature. In order to illustrate the proficiency of the proposed approach, it is compared to other well-known evolutionary methods. The comparison investigates several test functions that are commonly considered within the literature of evolutionary algorithms. The result shows a high performance and effectiveness of this method for searching a global optimum, as well as the cost reduction noticeably for various benchmark functions. [ABSTRACT FROM AUTHOR]

A three unequal area interconnected power system under deregulated environment having multi-sources of generation is considered. The first area consists of solar thermal and thermal unit, the second area consists of thermal and hydro unit, and the third area contains thermal and gas units as generating units with proper generation rate constraint (GRC). Cascaded proportional integral-proportional derivative with filter coefficient (CPIPDF) is employed as a secondary controller. A recent meta-heuristic algorithm called moth flame optimization (MFO) is considered to optimize the controller parameters. System performances are compared with the classical controllers like proportional-integral (PI), proportional-integral-derivative (PID), proportional-integral-derivative with filter coefficient (PIDF) and cascaded proportional-integral-proportional-derivative (CPIPD) controller. MFO tuned CPIPDF controller performs better than all other controllers considered in this paper for different electrical transactions. Robustness of the optimum gains of CPIPDF controller obtained at poolco transaction condition is evaluated. In addition, system performance is studied with variation in disco participation matrix (DPM). [ABSTRACT FROM AUTHOR]

Most of the metaheuristics can efficiently solve unconstrained problems; however, their performance may degenerate if the constraints are involved. This paper proposes two constraint handling approaches for an emerging metaheuristic of Cohort Intelligence (CI). More specifically CI with static penalty function approach (SCI) and CI with dynamic penalty function approach (DCI) are proposed. The approaches have been tested by solving several constrained test problems. The performance of the SCI and DCI have been compared with algorithms like GA, PSO, ABC, d-Ds. In addition, as well as three real world problems from mechanical engineering domain with improved solutions. The results were satisfactory and validated the applicability of CI methodology for solving real world problems. [ABSTRACT FROM AUTHOR]

Optimization over the efficient set of a multi-objective optimization problem is a mathematical model for the problem of selecting a most preferred solution that arises in multiple criteria decision-making to account for trade-offs between objectives within the set of efficient solutions. In this paper, we consider a particular case of this problem, namely that of optimizing a linear function over the image of the efficient set in objective space of a convex multi-objective optimization problem. We present both primal and dual algorithms for this task. The algorithms are based on recent algorithms for solving convex multi-objective optimization problems in objective space with suitable modifications to exploit specific properties of the problem of optimization over the efficient set. We first present the algorithms for the case that the underlying problem is a multi-objective linear programme. We then extend them to be able to solve problems with an underlying convex multi-objective optimization problem. We compare the new algorithms with several state of the art algorithms from the literature on a set of randomly generated instances to demonstrate that they are considerably faster than the competitors. [ABSTRACT FROM AUTHOR]

The particle swarm optimisation (PSO) is a stochastic, optimisation technique based on the movement and intelligence of swarms. In this paper, three new effective optimisation algorithms BPSO, HPSO and WPSO, by incorporating some decision criteria into PSO, have been proposed and analysed both in terms of their efficiency, resistance to the problem of premature convergence and the ability to avoid local optima. In the new algorithms, for each particle except position, two sets of velocities are generated and the profit matrix is constructed. Using the decision criteria the best strategy is selected. Simulations for benchmark test nonlinear function show that the algorithms in which the decision criteria have been applied, are beneficial over classical PSO in terms of their performance and efficiency. [ABSTRACT FROM AUTHOR]

Multi-verse optimization algorithm (MVO) is one of the recent meta-heuristic optimization algorithms. The main inspiration of this algorithm came from multi-verse theory in physics. However, MVO like most optimization algorithms suffers from low convergence rate and entrapment in local optima. In this paper, a new chaotic multi-verse optimization algorithm (CMVO) is proposed to overcome these problems. The proposed CMVO is applied on 13 benchmark functions and 7 well-known design problems in the engineering and mechanical field; namely, three-bar trust, speed reduce design, pressure vessel problem, spring design, welded beam, rolling element-bearing and multiple disc clutch brake. In the current study, a modified feasible-based mechanism is employed to handle constraints. In this mechanism, four rules were used to handle the specific constraint problem through maintaining a balance between feasible and infeasible solutions. Moreover, 10 well-known chaotic maps are used to improve the performance of MVO. The experimental results showed that CMVO outperforms other meta-heuristic optimization algorithms on most of the optimization problems. Also, the results reveal that sine chaotic map is the most appropriate map to significantly boost MVO’s performance. [ABSTRACT FROM AUTHOR]

The 0–1 knapsack problem (KP) is a well-known intractable optimization problem with wide range of applications. Harmony Search (HS) is one of the most popular metaheuristic algorithms to successfully solve 0–1 KPs. Nevertheless, metaheuristic algorithms are generally compute intensive and slow when implemented in software. In this paper, we present an FPGA-based pipelined hardware accelerator to reduce computation time for solving large dimension 0–1 KPs using Binary Harmony Search algorithm. The proposed architecture exploits the intrinsic parallelism of population based metaheuristic algorithm and the flexibility and parallel processing capabilities of FPGAs to perform the computation concurrently thus enhancing performance. To validate the efficiency of the proposed hardware accelerator, experiments were conducted using a large number of 0–1 KPs. Comparative analysis on experimental results reveals that the proposed approach offers promising speedups of 51×– 111×as compared with a software implementation and 2×– 5×as compared with a hardware implementation of Binary Particle Swarm Optimization algorithm. Hardware Accelerator Implementation using Binary Harmony Search [ABSTRACT FROM AUTHOR]

In the real-world truss optimization problems, the optimal performance obtained using conventional deterministic methods can be dramatically degraded in the presence of sources of uncertainty. The source of uncertainty may be the variability of applied loads, spatial positions of nodes, and section and material properties. In this paper, we present a new theoretical model and a problem-specific metaheuristic approach when the only source of uncertainty is the variability of the applied load directions. The essence of the novel conception is independent from the theoretical description of the uncertainty which may be either probabilistic (stochastic) or possibilistic (fuzzy). In the presented unified (nonprobabilistic and nonpossibilistic) approach, the varying load directions are handled as uncertain-but-bounded parameters. The result of the optimization is a robust minimal-weight truss design, which is invariant to the investigated load uncertainty type. The well-known ten-bar plane truss example with the most popular direction settings will be used to illustrate the validity and efficiency of the presented approach. In the presented example we replaced each nominal load direction by an angle set around the nominal value. The detailed description of the problem-specific robust metaheuristic based on the previously developed ANGEL metaheuristic will be presented in a forthcoming paper. [ABSTRACT FROM AUTHOR]

A new optimization algorithm is presented to solve the stratification problem. Assuming the number L of strata and the total sample size n are fixed, we obtain strata boundaries by using an objective function associated with the variance. In this problem, strata boundaries must be determined so that the elements in each stratum are more homogeneous among themselves. To produce more homogeneous strata, this paper proposes a new algorithm that uses the Greedy Randomized Adaptive Search Procedure (GRASP) methodology. Computational results are presented for a set of problems, with the application of the new algorithm and some algorithms from literature. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is presented to obtain stable reduced-order model of higher order linear time invariant continuous systems using optimization technique. In order to perform optimization, a new meta-heuristic search algorithm, called Cuckoo Search (CS) is applied which is inspired by the natural behaviour of cuckoo species including the Lévy flight behaviour of some birds and fruit flies. The effectiveness of the proposed method is supported by some numerical examples of single input single output (SISO) systems and the results are compared with other well-known methods available in the literature in terms of most popular performance indices like integral square error, integral absolute error, and integral time absolute error. The method is extended for multi input multi output (MIMO) systems also. [ABSTRACT FROM PUBLISHER]

Mean-variance mapping optimization (MVMO) is a new population-based metaheuristic technique which is successfully applied for different power system optimization problems. The special feature of MVMO is the mapping function applied for the mutation based on the mean and variance of n-best population. Recently, the modified version of MVMO has been developed to become more powerful, named as swarmbased mean-variance mapping optimization (MVMOS). This paper proposes MVMOS as a new approach for solving the economic dispatch (ED) problem considering valve-point effects. To validate the performance of the proposed method, the MVMOS is tested on three systems including 3, 13, and 40 thermal generating units with valve-point effects and the obtained results from MVMOS are compared to those from other existing methods in the literature. Test results have indicated that the proposed MVMOS is more robust and produces better solution quality than many other methods. Therefore, the MVMOS is efficient for solving the ED with valve-point effects. [ABSTRACT FROM AUTHOR]