Hunger Games Search:
Strong search, efficient, fast, and secure balance
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Visions, Conception, Implementation, Deep Analysis, Perspectives, and Towards Performance Shifts
Developed for a better future by:
Yutao Yang, Huiling Chen, Ali Asghar Heidari, Amir H Gandomi
It is a population-based method with stochastic switching elements that enrich its main exploratory and exploitative behaviors and flexibility of HGS in dealing with challenging problem landscapes. The algorithm has been compared to LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods.
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A recent set of overused population-based methods have been published in recent years. Despite their popularity, most of them have uncertain, immature performance, partially done verifications, similar overused metaphors, similar immature exploration and exploitation components and operations, and an insecure tradeoff between exploration and exploitation trends in most of the new real-world cases. Therefore, all users need to extensively modify and adjust their operations based on main evolutionary methods to reach faster convergence, more stable balance, and high-quality results. To move the optimization community one step ahead toward more focus on performance rather than change of metaphor, a general-purpose population-based optimization technique called Hunger Games Search (HGS) is proposed in this research with a simple structure, special stability features and very competitive performance to realize the solutions of both constrained and unconstrained problems more effectively. The proposed HGS is designed according to the hunger-driven activities and behavioural choice of animals. This dynamic, fitness-wise search method follows a simple concept of “Hunger” as the most crucial homeostatic motivation and reason for behaviours, decisions, and actions in the life of all animals to make the process of optimization more understandable and consistent for new users and decision-makers. The Hunger Games Search incorporates the concept of hunger into the feature process; in other words, an adaptive weight based on the concept of hunger is designed and employed to simulate the effect of hunger on each search step. It follows the computationally logical rules (games) utilized by almost all animals and these rival activities and games are often adaptive evolutionary by securing higher chances of survival and food acquisition. This method's main feature is its dynamic nature, simple structure, and high performance in terms of convergence and acceptable quality of solutions, proving to be more efficient than the current optimization methods. The effectiveness of HGS was verified by comparing HGS with a comprehensive set of popular and advanced algorithms on 23 well-known optimization functions and the IEEE CEC 2014 benchmark test suite. Also, the HGS was applied to several engineering problems to demonstrate its applicability. The results validate the effectiveness of the proposed optimizer compared to popular essential optimizers, several advanced variants of the existing methods, and several CEC winners and powerful differential evolution (DE)-based methods abbreviated as LSHADE, SPS_L_SHADE_EIG, LSHADE_cnEpSi, SHADE, SADE, MPEDE, and JDE methods in handling many single-objective problems. We designed this open-source population-based method to be a standard tool for optimization in different areas of artificial intelligence and machine learning with several new exploratory and exploitative features, high performance, and high optimization capacity. The method is very flexible and scalable to be extended to fit more form of optimization cases in both structural aspects and application sides. This paper's source codes, supplementary files, Latex and office source files, sources of plots, a brief version and pseudocode, and an open-source software toolkit for solving optimization problems with Hunger Games Search and online web service for any question, feedback, suggestion, and idea on HGS algorithm will be available to the public at https://aliasgharheidari.com/HGS.html.
This study presents a novel population-based model to tackle optimization problems based on social animals' characteristics in searching for food. More specifically, in each iteration, the algorithm searches around the optimal location, in the same manner, that animals forage, where the weights, or hunger values, mimic the impact of hunger on an animal’s individual activity. Qualitative analysis of the algorithm was carried out using four indicators, including search history, the trajectory of the first dimension, average fitness, and convergence curve. The proposed Hunger Games Search (HGS) was validated on a comprehensive collection of 23 benchmark functions and IEEE CEC2014 functions. The Wilcoxon sign rank test and the Freidman test were utilized to assess the statistical significance between HGS and other well-known MAs. The experimental results show that HGS has an efficient searching ability compared with other algorithms, and it can quickly find and develop the target solution space. Overall, HGS is very good at balancing exploration and exploration. Simultaneously, to confirm the applicability of HGS to practical problems, four engineering problems were considered, including welded beam, I-beam, and multiple disk clutch brake. From the experimental results, HGS can satisfy the optimization effect of production engineering problems and significantly reduce manufacturing costs.
In this paper, we followed the most straightforward rules for developing HGS to make it easier to expand and integrate with existing artificial intelligence methods. There are many windows for the future directions of this new efficient HGS algorithm. First, researchers can investigate the effectiveness of this open-source HGS code for solving real-world problems in parameter optimization for machine learning models, binary feature selection, and image segmentation. Another window is how to enhance the performance of the basic version proposed in this research. Possible chances and future proposals are the application of oppositional based learning (OBL), Orthogonal learning (OL), chaotic signals instead of random variables, applying evolutionary population dynamic (EPD), advantages of mutation and crossover on the exploration and exploitation cores of the method, ensemble mutation-based strategies, role of levy flight, application of greedy search, co-evolutionary methods, quantum computing, parallel computing, ranking-based schemes, random spare schemes, multi-population structures, dimension-wise operations, and their various combination . Lastly, the proposed Hunger Games Search is a single-objective approach in the currently released version, and the next task can be to develop the binary, multiobjective and many-objective variants of the developed Hunger Games Search to deal with more variety of multiobjective, binary, and many-objective problems.
As a gradient-free, population-based optimizer, the proposed HGS exhibits efficient performance due to the following unique advantages:
1- It is a population-based method with stochastic switching elements that enrich its main exploratory and exploitative behaviours and flexibility of HGS in dealing with challenging problem landscapes.
2-The adaptive and time-varying mechanisms of HGS allow this method to handle multi-modality, and local optima problems more effectively.
3-The consideration of hunger ratio and influence of hunger on the range of activity make the HGS more flexible and capable of changing the performance in a fitness-wise fashion.
4-The application of individual fitness values enables HGS to consider historical info if it is required to change the behaviour.
5-Parameters l and E assist HGS in evolving the initial positions and search mode to ensure the exploration of the whole solution space as far as possible and enhance the diversification capacity of the algorithm to a great extent.
6-The hunger weights (W1 ) and (W2 ) increase the perturbation of HGS during the search process and prevent the algorithm from trapping in a local optimum.
7-The parameter R ensures that the search step of HGS is reduced at a specific rate; therefore satisfying the need to explore the target solution space in a broad range in the early stage and exploit the depth of the target search basin in the later stages.
8-The Hunger Games Search can evolve the search agents with regards to best solutions (Xb) and normal solutions (X), which is a simple idea to ensure more exploration patterns and more coverage on the hidden areas of the feature space.
9-The structure and logic of Hunger Games Search are straightforward, and it is easy to be integrated with other evolutionary mechanisms for dealing with new practical problems in science and engineering.
10-Despite the simple equations and compared to the existing methods, the Hunger Games Search has a very superior performance with high-quality results compared to well-known basic and advanced methods for studied benchmark problems.
11- The codes of Hunger Games Search will be publicly available in different languages, and users can easily access the software codes and apply it to their target problem based on functional programming.
An online, public web service at https://aliasgharheidari.com/HGS.html will be responsible for all users regarding any assistance and required supplementary material.
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How to cite?
Yutao Yang, Huiling Chen, Ali Asghar Heidari, Amir H Gandomi, "Hunger Games Search: Visions, Conception, Implementation, Deep Analysis, Perspectives, and Towards Performance Shifts." Expert Systems with Applications, 2021, 114864 https://doi.org/10.1016/j.eswa.2021.114864
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