Since its introduction in 1995, Differential Evolution (DE) has emerged as a foundational algorithm in the domain of computational intelligence and metaheuristic optimization. This paper presents a comprehensive bibliometric and thematic review of DE research over three decades (1995–2025), based on 9,900+ publications retrieved from Scopus, Web of Science (WoS), and IEEE Xplore. By using advanced visualization tools, including Sankey diagrams (to trace institution-country-keyword flows), Choropleth maps (to reveal global research distribution), citation and co-authorship networks, and heatmaps (to assess cross-domain influence), the review uncovers major contributors, thematic concentrations, and emerging frontiers. The analysis spans publication trajectories, prolific authors and institutions, core research directions, and domain-specific applications in 12 prominent fields such as engineering optimization, artificial intelligence, bioinformatics, energy systems, and control systems. The study emphasizes the evolution of DE and its increasing interdisciplinary integration, and the growing dominance of Asia, particularly China, India, and Iran, as key centers of DE innovation. Through a synthesis of keyword co-occurrence, collaborative clustering, and citation dynamics, this review maps the landscape of DE research and outlines pressing challenges and promising avenues for future inquiry.

Differential evolution (DE) has demonstrated its significant contribution to the optimization of different real-world applications as well as standard benchmarks. This paper presents a novel variant of the DE algorithm, known as the LSHADESPA algorithm. The LSHADESPA algorithm incorporates three significant modifications to enhance its performance. Firstly, a proportional shrinking population mechanism is employed to reduce the computational burden. Secondly, a simulated annealing (SA)-based scaling factor is introduced to improve the exploration properties of the algorithm. Finally, an oscillating inertia weight-based crossover rate is utilized to strike a balance between exploitation and exploration. These modifications aim to enhance the overall efficiency and effectiveness of the DE algorithm. The proposed LSHADESPA algorithm has been empirically evaluated on a set of benchmark problems, namely the CEC 2014, CEC 2017, CEC 2021, as well as CEC 2022. The experimental outcomes show that the LSHADESPA algorithm performs superior to other MH algorithms. Additionally, the Wilcoxon rank-sum as well as the Friedman rank test proves the statistical significance of the proposed LSHADESPA in comparison to other algorithms under comparison. The outcomes indicate that the LSHADESPA algorithm has statistical significance, with Friedman statistics for the CEC 2014, CEC 2017, and CEC 2022 benchmark functions achieving the lowest f-rank value compared to the other MH algorithms which are found to be 41, 77, and 26, respectively, and obtained 1st rank. Note that this paper is an invited extended version of the paper published in ISCMI 2022 conference.

This paper proposes a new multi-algorithm based evolution strategy with the addition of adaptive mutation operators for global optimization. The new algorithm namely Kepler meerkat naked (KMN) algorithm is based on Kepler’s optimization algorithm (KOA), meerkat optimization algorithm (MOA), and naked mole-rat algorithm (NMRA), as the core algorithms and, grey wolf optimizer (GWO) and cuckoo search (CS) inspired equations for enhanced exploration and exploitation. The proposed algorithm uses six new mutation operators for parametric enhancements, and follows an iterative division mechanism for a balanced operation. A comparative analysis is done with respect to classical benchmarks, CEC 2014, CEC 2017, CEC 2019 and CEC 2022 benchmark datasets for performance evaluation. Six engineering design problems are also used to test the performance of the proposed KMN algorithm for constraint optimization. Apart from that, a binary version of KMN namely bKMN is also proposed, and ten feature selection datasets are used for performance evaluation. Performance testing of the KMN and bKMN algorithm is done with success history-based DE (SHADE), LSHADE-SPACMA, self-adaptive DE (SaDE), fast opposition-based learning golden jackal optimization (FROBL-GJO), LSHADE-EpSin, jSO, EBOwithCMAR, among others. Experimental and statistical results are performed using Wilcoxon’s and Friedman’s tests, and it has been found that the proposed algorithms are highly competitive in contrast to other algorithms under study.

This paper proposes a multi-strategy, multiple algorithms based hybrid strategy using flower pollination algorithm (FPA), grey wolf optimizer (GWO), INFO, and naked mole rat algorithm (NMRA). The proposed algorithm, named the Flower Grey INFO Naked (FGIN) algorithm, incorporates the most effective equations from the FPA, GWO, INFO, and NMRA. Here FPA’s basic structure following global and local search is used, GWO is meant for providing extensive exploration, whereas INFO and NMRA both contribute towards exploitative search. Dynamic iterative search and population segmentation strategies are incorporated for enhanced performance of FGIN algorithm. For enhanced self-adaptivity, six mutation weight operators are applied to the three parameters of the proposed strategy. FGIN is also subjected to higher dimensional and variable population analysis, and has been found to be highly effective. A deeper analysis using CEC 2005, CEC 2017, CEC 2019 and CEC 2022 benchmark data set is also performed to validate the superiority of the proposed algorithm with respect to success history based differential evolution (SHADE), self-adaptive DE (SaDE), NL-LSHADE-LBC, DE with active archive (JADE), LSHADE-SPACMA, evolutionary algorithms with eigen crossover (EA4eig), extended GWO (GWO-E), NL-LSHADE-RSP-MID, jDE100, and others. The proposed FIGN algorithm is then used for the parametric identification of proton exchange membranes in fuel cells (PEMFC). The optimization challenge of PEMFC is to minimize the sum of squared error (SSE) between the experimental and measured voltage. And also determine, the optimal values of seven unknown parameters for the PEMFC stack’s. To illustrate the potential of the FGIN algorithm is validated by utilizing five well-known commercial PEMFCs, namely NedStack PS6, BCS 500 W, Stack 250 W, Ballard Mark V, and Horizon H-12 Stack. In order to check the effectiveness of the FGIN algorithm, both parametric and non-parametric statistical tests have been conducted, and it has been found that the proposed algorithm performs significantly better.

Non-pharmaceutical interventions (NPIs) are effective measures to contain a pandemic. Yet, such control measures commonly have a negative effect on the economy. Here, we propose a macro-level approach to support resolving this Health-Economy Dilemma (HED). First, an extension to the well-known SEIR model is suggested which includes an economy model. Second, a bi-objective optimization problem is defined to study optimal control policies in view of the HED problem. Third, four multi-objective evolutionary algorithms are applied to perform a study on the health-economy performance trade-offs that are inherent to the obtained optimal policies. Finally, the results from the applied algorithms are compared to select a preferred algorithm for future studies. As expected, for the proposed models and strategies, a clear conflict between the health and economy performances is found. Furthermore, the results suggest that the guided usage of NPIs is preferable as compared to refraining from employing such strategies at all. This study contributes to pandemic modeling and simulation by providing a novel concept that elaborates on integrating economic aspects while exploring the optimal moment to enable NPIs.

Cuckoo search (CS) is a highly competitive single objective optimization technique. The algorithm has been widely applied in various diverse application domains and has been found to be efficient in solving various real-life problems. In the present work, we have proposed a new enhanced version of CS algorithm and tested its performance on recently proposed CEC 2017 and CEC 2020 benchmark test problems. The proposed algorithm has been named as CSsin and it employs four major modifications, i) new techniques for global and local search are devised, ii) dual search strategy is followed to enhance exploration and exploitation properties of CS algorithm, iii) a linearly decreasing switch probability has been used to add a balance between local and global search, and iv) linearly decreasing population size is used to reduce the computational burden. Apart from these modifications, the division of iterations has been employed as a further modification. The CSsin algorithm has been tested on IEEE CEC 2017 and CEC 2020 benchmark test problems having various dimension sizes and a comparative study has been performed with respect to state-of-the-art optimization algorithms for single objective bound constraint optimization problems. The results of statistical significance test affirm the competitiveness of the proposed algorithm with respect to state-of-the-art techniques.

Differential Evolution algorithm is very challenging algorithm and has been found to put forth the basis of evolutionary computation. This algorithm because of its simple structure and linear nature, has been applied to a large number of optimization problems from various diversified fields. In this paper, we propose a new variant of DE by modifying the original LSHADE-cnEpSin algorithm. Two new modifications are proposed, keeping all the modifications of LSHADE-cnEpSin intact. The modifications proposed include the introduction of adaptive parameters by using Weibull distribution based scaling factor and exponentially decreasing crossover rate. Apart from that linearly decreasing population size is also used. The main reason for these adaptations is to make an adaptive algorithm so that no parameter needs to be changed from the end user perspective. The proposed algorithm has been applied to solve CEC2017 and CEC2019 benchmark problems. The numerical results prove that the newly proposed SALSHADE-cnEpSin algorithm performs better than SaDE, JADE, SHADE, LSHADE, CV1.0, CVnew, MVMO and other algorithms.

Cuckoo Search is a nature inspired evolutionary algorithm to solve real-world optimization problems. It is inspired from the brood parasitism of cuckoos. It is highly competitive and has been used to solve number of problems in the field of science and engineering. A number of modifications have been proposed to enhance its performance in the past. This paper presents an improved version of CS namely CVnew in which three modifications are proposed. The first modification is the introduction of two new search equations to improve the global search while the second one deals with the incorporation of four search equations to improve the local search. As a third modification, a balance between global and local search has been increased by exponentially decreasing the switch probability. The proposed algorithm has been applied to solve single objective real-parameter problems of CEC 2017. The numerical results prove the better performance of CVnew in comparison with SaDE, JADE, SHADE and MVMO.