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Optimal placement of static VAR compensator (SVC) devices not only improves the voltage profile (VP) but also reduces the active power loss (APL) and enhances the voltage stability (VS) through injecting appropriate VARs at optimal buses. The traditional mathematical methods may not provide global best solution and pose difficulties in handling multi-objective SVC placement (SVCP) problem with complex constraints and forcefully place all the given number of SVCs in the system without assessing their real requirements in enhancing the chosen performances. The purpose of this paper is to formulate the SVCP as a multi-objective optimization problem and solve it using a metaheuristic algorithm for global best solution.

The proposed SVCP method uses improved harmony search optimization (IHSO) with dissonance-avoiding mechanism for obtaining the global best solution through driving away the solution from the sub-optimal traps. In addition, the method uses a self-adaptive technique for optimally tuning the IHSO parameters and places only the required number of SVCs from the given number of SVCs.

This paper presents the results of the proposed method for 14, 30 and 57 bus systems and exhibits that the proposed method outperforms the existing SVCP methods in achieving the desired performances.

This paper proposes a new self-adaptive IHSO based SVCP method for optimally placing only the required number of SVCs with a goal of attaining the global best performances.

The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller.

The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure.

First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem.

The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

Assembly sequence optimization is a difficult combinatorial optimization problem having to simultaneously satisfy various feasibility constraints and optimization criteria. Applications of evolutionary algorithms have shown a lot of promise in terms of lower computational cost and time. But there remain challenges like achieving global optimum in least number of iterations with fast convergence speed, robustness/consistency in finding global optimum, etc. With the above challenges in mind, this study aims to propose an improved flower pollination algorithm (FPA) and hybrid genetic algorithm (GA)-FPA.

In view of slower convergence rate and more computational time required by the previous discrete FPA, this paper presents an improved hybrid FPA with different representation scheme, initial population generation strategy and modifications in local and global pollination rules. Different optimization objectives are considered like direction changes, tool changes, assembly stability, base component location and feasibility. The parameter settings of hybrid GA-FPA are also discussed.

The results, when compared with previous discrete FPA and GA, memetic algorithm (MA), harmony search and improved FPA (IFPA), the proposed hybrid GA-FPA gives promising results with respect to higher global best fitness and higher average fitness, faster convergence (especially from the previously developed variant of FPA) and most importantly improved robustness/consistency in generating global optimum solutions.

It is anticipated that using the proposed approach, assembly sequence planning can be accomplished efficiently and consistently with reduced lead time for process planning, making it cost-effective for industrial applications.

Different representation schemes, initial population generation strategy and modifications in local and global pollination rules are introduced in the IFPA. Moreover, hybridization with GA is proposed to improve convergence speed and robustness/consistency in finding globally optimal solutions.

The purpose of this research is to design and develop a technique for polyphase code design for the radar system.

The proposed fractional harmony search algorithm (FHSA) performs the polyphase code design. The FHSA binds the properties of the harmony search algorithm and the fractional theory. An optimal fitness function based on the coherence and the autocorrelation is derived through the proposed FHSA. The performance metrics such as power, autocorrelation and cross-correlation measure the efficiency of the algorithm.

The performance metrics such as power, autocorrelation and cross-correlation is used to measure the efficiency of the algorithm. The simulation results show that the proposed optimal phase code design with FHSA outperforms the existing models with 1.420859, 4.09E−07, 3.69E−18 and 0.000581 W for the fitness, autocorrelation, cross-correlation and power, respectively.

The proposed FHSA for the design and development of the polyphase code design is developed for the RADAR is done to reduce the effect of the Doppler shift.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

The purpose of this paper is to introduce a chaotic harmony search (CHS) approach based on the chaotic Zaslavskii map to parameters identification of Jiles-Atherton vector hysteresis model.

In laminated magnetic cores when the magnetic flux rotates in the lamination plane, one observes an increase in the magnetic losses. The magnetization in these regions is very complex needing a vector model to analyze and predict its behavior. The vector Jiles-Atherton hysteresis model can be employed in rotational flux modeling. The vector Jiles-Atherton model needs a set of five parameters for each space direction taken into account. In this context, a significant amount of research has already been undertaken to investigate the application of metaheuristics in solving difficult engineering optimization problems. Harmony search (HS) is a derivative-free real parameter optimization metaheuristic algorithm, and it draws inspiration from the musical improvisation process of searching for a perfect state of harmony. In this paper, a CHS approach based on the chaotic Zaslavskii map is proposed and evaluated.

The proposed CHS presents an efficient strategy to improve the search performance in preventing premature convergence to local minima when compared with the classical HS algorithm. Numerical comparisons with results using classical HS, genetic algorithms (GAs), particle swarm optimization (PSO), and evolution strategies (ES) demonstrated that the performance of the CHS is promising in parameters identification of Jiles-Atherton vector hysteresis model.

This paper presents an efficient CHS approach applied to parameters identification of Jiles-Atherton vector hysteresis model.

The purpose of this study is to provide the reader with a full study of the bat algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains and all the studies that assess its performance against other meta-heuristic algorithms.

Bat algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, back-propagation neural network, harmony search algorithm, differential evaluation strategies, enhanced particle swarm optimization and Cuckoo search algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (modified bat algorithm, enhanced bat algorithm, bat algorithm with mutation (BAM), uninhabited combat aerial vehicle-BAM and non-linear optimization). It also provides a summary review that focuses on improved and new bat algorithm (directed artificial bat algorithm, complex-valued bat algorithm, principal component analyzes-BA, multiple strategies coupling bat algorithm and directional bat algorithm).

Shed light on the advantages and disadvantages of this algorithm through all the research studies that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it.

As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm covering all its aspects.

The computational drawbacks of existing numerical methods have forced researchers to rely on heuristic algorithms. Heuristic methods are powerful in obtaining the solution of optimization problems. Although they are approximate methods (i.e. their solution are good, but not provably optimal), they do not require the derivatives of the objective function and constraints. Also, they use probabilistic transition rules instead of deterministic rules. The purpose of this paper is to present an improved ant colony optimization (IACO) for constrained engineering design problems.

IACO has the capacity to handle continuous and discrete problems by using sub‐optimization mechanism (SOM). SOM is based on the principles of finite element method working as a search‐space updating technique. Also, SOM can reduce the size of pheromone matrices, decision vectors and the number of evaluations. Though IACO decreases pheromone updating operations as well as optimization time, the probability of finding an optimum solution is not reduced.

Utilizing SOM in the ACO algorithm causes a decrease in the size of the pheromone vectors, size of the decision vector, size of the search space, the number of function evaluations, and finally the required optimization time. SOM performs as a search‐space‐updating rule, and it can exchange discrete‐continuous search domain to each other.

The suitability of using ACO for constrained engineering design problems is presented, and applied to optimal design of different engineering problems.

This paper aims to present a technique for optimal trajectory planning of industrial robots that applies a new harmony search (HS) algorithm.

The new HS optimization algorithm adds one more operation to the original HS algorithm. The objective function to be minimized is the trajectory execution time subject to kinematical and mechanical constraints. The trajectory is built by quintic B‐spline curves and cubic B‐spline curves.

Simulation experiments have been undertaken using a 6‐DOF robot QH165. The results show that the proposed technique is valid and that the trajectory obtained using quintic B‐spline curves is smoother than the trajectory using cubic B‐spline curves.

The proposed new HS algorithm is more efficient than the sequential quadratic programming method (SQP) and the original HS method. The proposed technique is applicable to any industrial robot and yields smooth and time‐optimal trajectories.

The purpose of this paper is to investigate the optimized setting of wire-cut electrical discharge machining (WEDM) parameters at which material removal rate (MRR) and mean roughness depth (Rz) set a compromise. The problem in the processing of Ti-6Al-4V by conventional processes is a high strength, high hardness, high tool wear. Due to which WEDM is adopted to machine Ti-6Al-4V biomedical alloy. Ti-6Al-4V alloy has a number of applications in the engineering and medical industries due to its high strength biocompatibility.

The effect of control factors (i.e. pulse on-time: Pon; pulse off-time: Poff; servo voltage: SV) on the MRR and Rz is investigated in the present research. The planning of experiments is done using a Taguchi-based L9 orthogonal array. The percentage influence of each factor on responses is also evaluated. The multi-objective optimization is done using the grey approach initially. After that, the results were also calculated using harmony search (HS). Therefore, a hybrid approach of grey and HS is used to find the optimized values of MRR and Rz.

The maximum value of grade calculated by grey-HS is 0.7879, while in the case of the experimental run the maximum value of grey grade is 0.7239. The optimized setting after improvisation at this grade value is Pon: 130 µs; Poff: 45 µs and SV: 70 V for MRR and Rz collectively. The validation of the suggested setting is completed by experimentation. The values of MRR and Rz are coming out to be 6.4 mm³/min and 13.84 µm, which represents improvised results after the implementation of the HS algorithm.

The integration of the grey approach with the HS principle in the manufacturing domain is yet to be explored. Therefore, in the present research hybrid approach of grey-HS is implemented in the manufacturing domain having applications in medical industries.