Self-adaptive Multi-population Research Articles

A comprehensive simultaneous allocation algorithm of charging stations and vehicle to grid operation in radial networks

The widespread adoption of electric vehicles (EVs) helps improve air quality by minimizing pollutants and promoting sustainable transportation practices. The integration of a substantial fleet of EVs leads to an increase in the installation of charging stations. The unplanned allocation of these loads impacts the distribution systems, such as increasing power loss. This paper introduces an algorithm that proposes an optimal allocation strategy for charging stations (CSs) in a distribution system. The allocation process aims to minimize the total apparent energy losses and ensure that the system voltage profile remains within limits. Load profile, charging and discharging profiles of CSs are considered. Vehicle-to-Grid (V2G) mode is one of the merits of integrating EVs in the grid, so this feature has been used to maintain system voltage stability and minimize energy loss. Power rating and locations of V2G mode are optimally determined to guarantee power quality indices. A hybrid algorithm of genetic algorithm (GA) and Self-Adaptive Multi-Population Elitist JAYA (SAMPE-JAYA) is developed to simultaneously allocate CSs and V2G in the systems. The proposed algorithm is verified on standard systems, IEEE 33, and 69-bus systems. The proposed algorithm is verified on standard IEEE 33-bus and 69-bus systems. V2G integration with CSs results in a reduction of energy losses by 6.33% and 22.25%, respectively, and voltage deviation improvements to 7.61% and 7.88% for the two systems.

SAMP Rao algorithm based minimization of the roughness of milled surface of Ti-6Al-4V

In order to solve optimization problems including machining responses as objectives, this study suggests a parameter-less method called the self-adaptive multi population (SAMP) Rao algorithm that does not rely on metaphors. When machining titanium alloys, achieving a good surface quality is a difficult process. In the current study, an effort has been made to reduce surface roughness during milling Ti-6Al-4V. Response surface methodology (RSM) was used in the experiment design to create a model for surface roughness using cutting parameters as variables. The developed model was tested in additional tests in addition to the primary experiments. It was shown that cutter speed and feed rate had the biggest effects on milled roughness, whereas depth of cut had very little of an impact. The model’s quality is demonstrated by the correlation coefficient (R2) 98%, which indicates that the model can explain 98% of the data. Later, a response surface-based desirability technique was used to minimize roughness of milled surface. The outcome of the proposed algorithm is compared with RSM optimizer. It has been noted that the outcomes achieved with the SAMP approach are more advantageous than RSM approach and found 18% minimum value of roughness of milled surface. Main reason to get better result in case of SAMP Rao algorithm is that it is based on the iterative search process with random values of cutting parameters with in defined range. SAMP Rao Algorithm provides cutting settings of 133.5 m min−1, 0.13 mm/tooth feed rate, and 2.06 mm of milling depth along with a minimal roughness of milled surface of 0.37 μm.

Multi-Objective Optimization of Welding Processes Using Modified Rao Algorithms

The welding community faces a challenging problem in choosing the best welding methods since they are multi-input processes. Modern manufacturing industries have requirements to get fast and optimum process parameters to utilize complete resources in an optimum way. This work attempts to improve the performance of submerged arc welding (SAW), friction welding (FW), and gas tungsten arc welding (GTAW) processes by optimizing their parameters. The newly developed Rao algorithms and their modified versions known as quasi-oppositional Rao (QO Rao), self-adaptive multi-population elite Rao (SAMPE Rao), and improved Rao (I-Rao) are used. This paper contains four multi-objective optimization case studies of SAW, FW, and GTAW processes. A weighted approach is employed to tackle the multi-objective optimization problems effectively. The outcomes achieved using the Rao, QO Rao, SAMPE Rao, and I-Rao algorithms are compared with those obtained by the established optimization algorithms such as accelerated cuckoo optimization algorithm (ACCOA), cuckoo optimization algorithm (COA), plant propagation algorithm (PPA), teaching–learning-based optimization (TLBO) algorithm, Jaya algorithm, quasi-oppositional Jaya (QO Jaya) algorithm, genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and heat transfer search (HTS) algorithm. The effectiveness of the Rao algorithms and their modified versions has been clearly demonstrated as these algorithms have provided superior solutions while requiring fewer generations to achieve them.

A novel self-adaptive multi-population quadratic approximation guided jaya for solving real-parameter constrained optimization problems

Despite the inherent stability of Quadratic Approximation based Jaya (JaQA) in solving constrained optimization problems, its effectiveness in exploring the search space is limited. Furthermore, the user-defined penalty parameter utilized to mitigate constraint violation poses another impediment to the efficiency of JaQA. In light of these drawbacks, this paper introduces a new meta-heuristic algorithm, namely Self-adaptive Multi-population Quadratic Approximation guided Jaya (SMP-JaQA). The three additional key features of the SMP-JaQA over JaQA are (i) the inclusion of a multi-population technique to ensure sufficient diversity in the search process (to address the exploration), (ii) a self-adaptation strategy to make it more user-friendly for complex problems in various domains (to address the exploration further), and (iii) a dynamic penalty factor (to automate the penalty parameter). These novel changes help strike a balance between exploration and exploitation too by identifying potential areas in the search space and reduces the likelihood of getting stuck in a local optima. The validity of SMP-JaQA is tested over 18 and 28 benchmark functions of varying dimensions from the CEC-2010 and CEC-2017 test suites, respectively. In order to justify the wide applicability of the proposed algorithm, a set of four real-life constrained problems (includes two mechanical design problems, an electronics design problem and an agricultural problem) are being solved through SMP-JaQA. A thorough analysis of computational outcomes, exploration–exploitation phases, and scalability are presented to shed light on the efficacy and stability levels of SMP-JaQA. The results are compared with those of other state-of-the-art meta-heuristics using Friedman's and Wilcoxon Signed-Rank tests. The outcomes indicate that SMP-JaQA produces reasonable solutions in terms of global optimality and solution quality.

Self-adaptive metaheuristic optimization technique for multi-objective reservoir operation

Abstract Multi-objective reservoir operation presents a number of critical challenges that must be overcome for efficient management of water resources. The inherent contradiction between several goals, such as satisfying irrigation demand and maximizing hydropower generation, is one of the major issues. Trade-offs and compromises must be carefully considered to balance these objectives. To solve this problem, a study was carried out to optimize the operation of multi-objective reservoirs with two primary goals: minimizing irrigation deficits and maximizing hydropower generation. This study employs the self-adaptive multipopulation multi-objective Jaya algorithm (SAMP-MOJA), an improved version of the Jaya algorithm, to construct an optimal Pareto Front utilizing an a priori approach. The performance of SAMP-MOJA is compared to that of other algorithms such as multi-objective particle swarm optimization, multi-objective invasive weed optimization, and multi-objective Jaya algorithm. The results of this study demonstrate that the hydropower generated by the developed model surpasses 80% of the actual generation. The study's findings will aid in designing the most effective Pareto front possible.

Optimal design and control loop selection for a STATCOM wide-area damping controller considering communication time delays

Wide-area damping controllers (WADC) have been shown to be effective in damping interarea oscillation modes. The varying nature of power system operating point requires the design of an adaptive WADC. Majority of the existing control design techniques rely on empirical approaches at one or more of their design stages, which makes it difficult to implement in adaptive control techniques. Therefore, the purpose of this study is to provide a systematic procedure based entirely on numerical approaches for designing a STATCOM-based WADC. The proposed procedure involving control loop selection, control design and adaptive time delay compensation, can then be implemented in adaptive WADC schemes. Geometric analysis has been used to choose the STATCOM location. Then, a novel optimization-based signal selection methodology is proposed. The proposed methodology employs a combination of multiple input signals to optimally generate a signal with the required modal objectives like enhancing observability and reducing interaction between different control loops. After that, another optimization problem is formulated to tune the controller parameters using integral of time multiplied by absolute error (ITAE) as the optimization objective. Both optimization problems have been evaluated using two optimization techniques: Particle swarm optimization (PSO) and self-adaptive multi-population elitist Jaya algorithm (SAMPE-JAYA). Finally, an adaptive delay compensator is designed to mitigate the effects of communication time delays. The proposed control strategy is applied on the IEEE 68-bus system and the controller performance is evaluated by modal analysis and multiple time-domain simulation scenarios.

Proportional-Integral-Derivative Controller Based-Artificial Rabbits Algorithm for Load Frequency Control in Multi-Area Power Systems

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller’s design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA.

An efficient evolutionary optimizer for solving complex dairy feed optimization problems

Dairy feed ration optimization is one of the most debated problems of the present time. The presence of linear and non-linear constraints, along with their various potential blends, turns the problem into Non-deterministic Polynomial (NP) hard. This triggers the need for efficient evolutionary algorithms to figure out feed mix at minimum cost and maximum shelf life for dairy cattle. Despite its exclusive importance in a real-life scenario, few papers have developed algorithms to solve this problem. Therefore, an attempt is made in this study to introduce a Self-adaptive multi-population approach with the recently proposed Quadratic Approximation based Jaya (JaQA) algorithm (Das et al., 2022), where care has been taken for the maintenance of diversity in each of its sub-population along with enhancement of convergence. However, considering the difficulty in setting the size of the sub-population, the Self-adaptive Multi-Population Quadratic Approximation guided Jaya (SMP-JaQA) follows an automated learning approach based on the past data to step in the next generation, hence making it a user-friendly approach. The performance of this approach is tested on problems in the benchmark set of Congress on Evolutionary Computation (CEC) 2006 and three popular engineering design functions. Numerical results, statistical tests (including Friedman Rank Test, Iman-Davenport Test, and Wilcoxon signed-rank test), and the convergence analysis confirm the better efficacy of the proposed technique over some recent algorithms. Later, this approach is incorporated into two real-life case studies to optimize the cost of feedstuffs for dairy cattle. The improved results under the feasible domain ascertain the proposed method to be farmer-friendly.

Robust Iteration-dependent Least Mean Square-based Distribution Static Compensator Using Optimized PI Gains

A robust iteration-dependent least mean square (RIDLMS) algorithm-based fundamental extractor is developed to estimate the fundamental components of the load current for a four-wire DSTATCOM with a nonlinear load. The averaging parameter for calculating the variable step size is iteration dependent and uses variable tuning parameters. Rather than using the current value, the previous learning rate was used in this method to achieve a more adaptive solution. This additional control factor aids in determining the exact learning rate, resulting in reliable and convergent outcomes. Its faster convergence rate and the avoidance of local minima make it advantageous. The estimation of the PI controller gains is achieved through a self-adaptive multi-population algorithm. The adaptive change in the group number will increase exploration and exploitation. The self-adaptive nature of the algorithm was used to determine the subpopulation number needed according to the fitness value. The main advantage of this self-adaptive nature is the multi-population spread throughout the search space for a better optimal solution. The estimated gains of the PI controllers are used for the DC bus and AC terminal voltage error minimization. The RIDLMS-based control with PI gains obtained using the proposed optimization algorithm showed better power quality performance. The considered RIDLMS-supported control was demonstrated experimentally using d-SPACE-1104.

Design optimization of shell-and-tube heat exchanger using Rao algorithms and their variants

• Rao algorithm and its variants are applied for design optimization of STHE. • Multi objective problem for achieving minimum cost and maximum effectiveness is computed. • Better results are found when compared with other state of the art algorithms. Shell-and-tube heat exchangers (STHEs) are widely used type of heat exchangers in the industrial applications. Due to the high usage, it is essential to design the STHEs with minimum cost possible. The traditional method for achieving minimum cost involved manual calculations and re-iterating the same by changing few parameters until obtained the minimum solution. However, this method would require lots of efforts and time but ultimately it would still not guarantee the optimum solution. In this work, the design of STHE is obtained with the help of Rao Algorithms and their variants, which would bring the best solutions for the problem with a simple approach. Rao algorithms collectively include Rao-1 algorithm, Rao-2 algorithm and Rao-3 algorithm. The salient feature of these algorithms is that they do not require any algorithm-specific parameters and hence without working on the algorithm for finding its best suitable parameters, one can immediately apply the algorithm easily. These algorithms use the best and worst solutions for finding the optimal solution in the search space with the help of random interactions between the alternative solutions. The self-adaptive multi-population Rao (SAMP Rao) algorithms are the variants of Rao algorithms, and these algorithms adapt the population size automatically based on the fitness value of the objective function, thus eliminating the tuning of population size. In this study, four different case studies have been taken into consideration and the design optimization of these four shell-and-tube heat exchangers for minimum cost is performed using Rao and SAMP Rao algorithms. The cost includes the fixed and operating costs and is calculated from the pumping power. Discrete variables are also included in the study to have the advantage of these in real-life applications. These studies were previously carried out using seven different optimization algorithms and the results obtained now by Rao Algorithms and SAMP Rao algorithms are compared with them. The results showed a significant improvement in minimizing the cost of STHE. For multi-objective optimization, total cost and effectiveness are targeted and their solutions are obtained in the form of Pareto front. It is found that the Rao algorithms and the SAMP Rao algorithms have provided improved results compared to the other optimization algorithms.

Dimensional synthesis of four-bar mechanisms using Rao algorithms and their variants

This paper presents the dimensional synthesis of four-bar mechanisms for path generation. The synthesis of the four-bar mechanisms is proposed using nine advanced optimization algorithms, namely the Rao-1 algorithm, Rao-2 algorithm, Rao-3 algorithm, self-adaptive multi-population (SAMP) Rao-1 algorithm, SAMP Rao-2 algorithm, SAMP Rao-3 algorithm, quasi-oppositional-based (QO) Rao-1 algorithm, QO Rao-2 algorithm, and QO Rao-3 algorithm. Six different cases of synthesis of a four-bar mechanism are considered. The brief literature review based on the synthesis of mechanisms is also presented. The designs of mechanisms obtained using Rao algorithms and their variants are compared with the designs acquired using other advanced optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO) algorithm, differential evolution (DE), imperialist competitive algorithm (ICA), hybrid GA-DE algorithm, simulated annealing (SA), Malaga university mechanism synthesis algorithm (MUMSA), IMMa optimization algorithm (IOA), hybrid Taguchi random coordinate search algorithm (HTRCA), hybrid GA and fuzzy logic (GA–FL) algorithm, ant-gradient method (AGM), teaching–learning-based optimization algorithm (TLBO), and hybrid Lagrange interpolation differential evolution algorithm (HLIDE). The comparison of results reveals the efficacy of Rao algorithms and their variants for synthesizing the four-bar mechanism.

Multi-objective reservoir operation of the Ukai reservoir system using an improved Jaya algorithm

Abstract This paper introduces an effective and reliable approach based on a multi-population approach, namely the self-adaptive multi-population Jaya algorithm (SAMP-JA), to extract multi-purpose reservoir operation policies. The current research focused on two goals: minimizing irrigation deficits and maximizing hydropower generation. Three different models were formulated. The results were compared with those for an ordinary Jaya algorithm (JA), particle swarm optimization (PSO), and an invasive weed optimization (IWO) algorithm. In Model-1, the minimum irrigation deficit obtained by SAMP-JA and JA was 305092.99 . SAMP-JA was better than JA, PSO and IWO in terms of convergence. In Model-2, the maximum hydropower generation achieved by SAMP-JA, JA and PSO was 1723.50 . When comparing the average hydropower generation, SAMP-JA and PSO performed better than JA and IWO. In terms of convergence, SAMP-JA was better than PSO. In Model-3, a self-adaptive multi-population multi-objective Jaya algorithm (SAMP-MOJA) was better than multi-objective particle swarm optimization (MOPSO) and multi-objective Jaya algorithm (MOJA) in terms of maximum hydropower generation, and MOPSO was better than SAMP-MOJA and MOJA in terms of minimum irrigation deficiency. While comparing convergence, SAMP-MOJA was found to be better than MOPSO and MOJA. Overall, SAMP-JA was found to outperform JA, POS and IWO.

Parameters Estimation of Photovoltaic Cells Using Self-adaptive Multi-population Rao Optimization Algorithm

Parameters Estimation of Photovoltaic Cells Using Self-adaptive Multi-population Rao Optimization Algorithm

An elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping similar documents

In this digital era, grouping similar documents from the archives on the web is a difficult and computationally expensive task. In this paper, we propose an elitism based self-adaptive multi-population Poor and Rich optimization algorithm for grouping the similar documents, referred to as ESAMPRO. The objective function of the proposed work maximizes the accuracy and minimize the intra cluster distance. The proposed algorithm is evaluated using the various extrinsic cluster quality metrics. An in-depth analysis of the experimental results on four supervised benchmark datasets confirms that the proposed ESAMPRO algorithm outperformed the five well-known document clustering algorithms such as K-means, particle swarm optimization, whale optimization, dragonfly and grey wolf optimization algorithm.

Optimal Design and Control of SSSCs for TLs Considering Technical and Economic Indices Using GA and SAMPE-JAYA Algorithms

Improving transmission lines (TL) steady-state stability limit can be achieved by Static Synchronous Series Compensators (SSSC) that work as a converter-based compensator. The modification of the SSSC function to inject both active and reactive power into the TL improves its performance according to different indices. The TL improvement indices include the reduction of TL losses, the enhancement of dynamic stability margin and the improvement of voltage profile along the TL. The economic index is considered a vital factor in the design of SSSC devices installed in TLs. This paper proposes an objective function to design and control SSSC devices by considering various technical and economical indices. The formulation of the objective function requires power flow analysis in the compensated transmission line under different loading conditions and compensation levels. The proposed multi-objective function includes the transmission line losses, the dynamic stability span, and the cost of SSSC device. The Genetic Algorithm (GA) and Self-adaptive Multi-population Elitist Jaya Algorithm (SAMPE-JAYA) are proposed as optimization techniques to obtain the minimized value of the multi-objective function. This paper proposes the installation of two SSSC devices in TL that leads to the improvement of the design and control indexes. The proposed objective function and the optimization techniques are applied for tie-line (400kV, 400km) that connects between two networks.

Water Distribution Network Design using Hybrid Self-adaptive Multi-population Elitist Pollination Intelligence (HSAMPEPI) Jaya Algorithm

An optimization model to minimize the cost of designing water distribution network is presented in this study. The model was formulated to reduce the cost coefficient in a plumbing system. A new hybrid method of optimization was constructed by combining the search abilities of Jaya-based algorithm and pollination intelligence algorithm, and was used to solve the designed model. The model was implemented by obtaining geometrical information of a water distribution network layout stationed at Gaa Odota, Ilorin, Kwara State, Nigeria. Result obtained from the model showed a significant reduction in the cost coefficient compared to that of the study area.

Optimal Design of PID Controller Based Sampe-Jaya Algorithm for Load Frequency Control of Linear and Nonlinear Multi-Area Thermal Power Systems

Modern multi-area power systems are in persistent facing to imbalances in power generation and consumption which directly causes frequency and tie-line power fluctuations in each area. This paper deals with the load frequency control (LFC) problem where the control objective of regulating their error signals despite the presences of several external load disturbances. It proposes an optimal design of proportional integral derivative controller (PID) based on a novel version of Jaya algorithm called self-adaptive multi-population elitist (SAMPE) Jaya optimizer. A filter with derivative term is integrated with PID controller to alleviate the impact of noise in the input signal. A time domain based-objective functions are investigated such as integral time-multiplied absolute value of the error (ITAE) and integral of absolute error (IAE). Both SAMPE-Jaya and Jaya optimizers are employed to optimally tune the PID parameters for interconnected power systems comprising two non-reheat thermal areas. Three test cases are performed with various load disturbances in both areas individually and simultaneaously. Also, the practical physical constraints related to generation rate constraint (GRC) with its nonlinearity characteristics are taken into account. In addition, the obtained results using the designed PID controller based on SAMPE-Jaya are compared with various reported techniques. These simulated comparisons declare the great efficiency and the high superiority of the designed PID controller based on SAMPE-Jaya.

Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks

Abstract In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL-MCHS), a double-layered microchannel heat sink (DL-MCHS), and a plate-fin heat sink (PFHS) are attempted to investigate the effectiveness of the proposed algorithms in solving real-world complex engineering optimization problems. The results obtained using the proposed algorithms are compared with the results obtained using the well-known advanced optimization algorithms such as genetic algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle swarm optimization (PSO), teaching-learning-based algorithm (TLBO), Jaya algorithm, multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA-II), real-coded GA (RCGA), direction-based GA, self-adaptive multi-population (SAMP) Rao algorithms, and basic Rao algorithms. The proposed quasi-oppositional-based Rao algorithms are found superior or competitive to the other optimization algorithms considered.

Study of population partitioning techniques on efficiency of swarm algorithms

This paper presents a study of various population partitioning techniques and their effect on the efficiency of swarm algorithms. Population partitioning techniques based on different concepts have been studied. Prominent amongst them is self-adaptive multi-population (SAMP) technique where populations are added and deleted dynamically based on their diversity. This techniques start with a single randomly initialised population, called free population. After evolution, if the distance between solutions drops below a limit, it is considered to have converged. If all existing populations have converged, a new randomly generated population is added. SAMP keeps at least one free population at all times, hence ensuring the algorithm doesn’t get trapped in local optima. Another promising population partitioning technique studied is random partitioning, where a single population is divided into many smaller sub-populations randomly. Few extensions to the studied techniques are proposed, like an adaptive hierarchical partitioning technique, seed based partitioning with fixed seeds, random partitioning with master population, SAMP with random partitioning etc. All the studied and proposed techniques are compared over a set of benchmark functions. The strongest amongst all techniques was found to be SAMPR. SAMPR is a hybrid of self-adaptive multi-population (SAMP) technique and random partitioning where after every few generations all populations are combined together and re-partitioned randomly. Efficiency of SAMPR is validated over seven well-known swarm algorithms. Extensive comparisons are conducted over multiple benchmark functions, CEC′14 function set and 800 GKLS generated functions. Results establish the efficiency of the proposed technique for improving performance of swarm algorithms.

Self-adaptive Multi-population Rao Algorithms for Engineering Design Optimization

ABSTRACTThe performance of various population-based advanced optimization algorithms has been significantly improved by using the multi-population search scheme. The multi-population search process improves the diversity of solutions by dividing the total population into a number of sub-populations groups to search for the best solution in different areas of a search space. This paper proposes improved optimization algorithms based on self-adaptive multi-population for solving engineering design optimization problems. These proposed algorithms are based on Rao algorithms which are recently proposed simple and algorithm-specific parameter-less advanced optimization algorithms. In this work, Rao algorithms are upgraded with the multi-population search process to enhance the diversity of search. The number of sub-populations is changed adaptively considering the strength of solutions to control the exploration and exploitation of the search process. The performance of proposed algorithms is investigated on 25 unconstrained benchmark functions and 14 complex constrained engineering design optimization problems. The results obtained using proposed algorithms are compared with the various advanced optimization algorithms. The comparison of results shows the effectiveness of proposed algorithms for solving engineering design optimization problems. The significance of the proposed methods has proved using a well-known statistical test known as “Friedman test.” Furthermore, the convergence plots are illustrated to show the convergence speed of the proposed algorithms.