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 .

Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts

An efficient modified grey wolf optimizer with Lévy flight for optimization tasks

Differential Evolution (DE) is a state-of-the art global optimization technique. Considerable research effort has been made to improve this algorithm and apply it to a variety of practical problems. Nevertheless, analytical studies concerning DE are rather rare. This paper surveys the theoretical results obtained so far for DE. A discussion of genetic operators characteristic of DE is coupled with an overview of the population diversity and dynamics models. A comprehensive view on the current-day understanding of the underlying mechanisms of DE is complemented by a list of promising research directions.

Differential Evolution: A survey of theoretical analyses

Whale Optimization Algorithm (WOA) is a popular swarm-based algorithm with some spotted defects in its generated patterns during the searching phases. In this study, an enhanced WOA-based method is proposed in order to overcome the drawbacks of slow convergence speed and easy falling of WOA into the local optimum. The designed variant is called enhanced WOA (EWOA), which combines two strategies at the same time. First, a new communication mechanism (CM) is embedded into the basic WOA to promote the global optimal search ability and the exploitation tendency of the WOA. Then, the Biogeography-based Optimization (BBO) algorithm is partially utilized to harmonize the exploration and exploitation trends. A representative set of comprehensive benchmark cases and three engineering cases are utilized to verify the advantages of the proposed EWOA. The experimental results show that the exploration ability, exploitation ability, state of the balance, and convergence style of the algorithm has been improved significantly. Based on results, the proposed EWOA is a promising and excellent algorithm, and it has achieved better solution quality and faster convergence rate compared with other most advanced algorithms. For access to material and guide for users of this paper, we host an online page at https://aliasgharheidari.com .

Evolutionary biogeography-based whale optimization methods with communication structure: Towards measuring the balance

Recently, many intelligent algorithms have been proposed to find the best solution for complex engineering problems. These algorithms can search volatile and multi-dimensional solution spaces and find optimal answers timely. In this paper, a new meta-heuristic method is proposed that inspires the behavior of the swarm of birds called Coot. The Coot algorithm imitates two different modes of movement of birds on the water surface: in the first phase, the movement of birds is irregular, and in the second phase, the movements are regular. The swarm moves towards a group of leading leaders to reach a food supply; the movement of the end of the swarm is in the form of a chain of coots, each of coot which moves behind its front coots. The algorithm then runs on a number of test functions, and the results are compared with well-known optimization algorithms. In addition, to solve several real problems, such as Tension/Compression spring, Pressure vessel design, Welded Beam Design, Multi-plate disc clutch brake, Step-cone pulley problem, Cantilever beam design, reducer design problem, and Rolling element bearing problem this algorithm is used to confirm the applicability of this algorithm. The results show that this algorithm is capable to outperform most of the other optimization methods. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/89102-coot-optimization-algorithm .

A new optimization method based on COOT bird natural life model

The color image encryption algorithm based on the chaos theory is not strong enough. In this paper, we proposed a color image chaos encryption algorithm combining Cyclic Redundancy Check (CRC) and nine palace map. Firstly, the pixel data of the plain image were moved and shuffled based on the theory of nine palace map. And the R, G and B components were extracted and converted into a binary sequence matrix that was then cyclically shifted based on the technology of generating CRC code. Finally, the encrypted image was derived from the XOR operation with random key matrix. The average entropy of encrypted image by our algorithm is 7.9993, which is slight improved compared with the coupled hyper chaotic Lorenz algorithm in previous studies. In addition, the algorithm has the advantages of large key space, high key sensitivity, anti-robust attack, and feasible encryption efficiency.

Color image chaos encryption algorithm combining CRC and nine palace map

As nodes have limited resources in the socially aware networks, they will have strong selfish behaviors, such as not forwarding messages and losing packets, which will lead to poor network performance. Thus, an equivalent-exchange-based data forwarding incentive scheme (EEIS) will be proposed in this paper. It is main that messages forwarding will be abstracted into a transaction in EEIS. The buyer and seller respectively make a price about the message according to its own resource state and negotiate twice the pricing both side until they agree, then the buyer will send the message and pay a certain virtual currency to the seller. Otherwise, the next message will continue to be traded. Meanwhile, both parties’ resource status, wealth status and the price of messages must be open and transparent to prevent the nodes from making false pricing during the transaction. Ultimately, the experimental results show the delivery ratio about messages is improved significantly and verify the effectiveness of EEIS.

An Equivalent Exchange Based Data Forwarding Incentive Scheme for Socially Aware Networks

A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data

Research on AI security enhanced encryption algorithm of autonomous IoT systems

Because mobile devices only have the limited resource in socially aware networking, some network nodes are unwilling to sacrifice their resource to forward messages to others for free, thus forming selfish nodes. However selfish nodes will cause dropping in the message delivery rate and affect the network performance, so this paper has proposed a diversity-based selfish node detection algorithm (DSNDA). Firstly, it mainly applies the node forwarding willingness mechanism to detect whether the node is selfish. If the node has the inadequate resource, that is, it cannot forward any messages for others; then it will be selfish. If the node’s resource is enough and the node has forwarded messages to other nodes, it indicates that the node is not selfish. Otherwise, DSNDA algorithm will use reputation mechanism to check the node further. If the node has broken faith, it indicates that the node is selfish. On the contrary the message confirmation mechanism will be employed to make the final check on the node. If a message does not be received or discarded after the node’s receiving it, which proves that it has selfish behaviour. If not, the node is normal. It can be proved by the simulation experiment and results that the DSNDA algorithm has significantly improved the delivery rate of messages, comparing with other algorithms. At the same time, reducing the network delay, thus verifying the feasibility of DSNDA.

Zenggang Xiong

Papers

Color image chaos encryption algorithm combining CRC and nine palace map

An Equivalent Exchange Based Data Forwarding Incentive Scheme for Socially Aware Networks

A privacy-preserving aggregation scheme based on negative survey for vehicle fuel consumption data

Research on AI security enhanced encryption algorithm of autonomous IoT systems

A Diversity-based Selfish Node Detection Algorithm for Socially Aware Networking