The emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

Based on the clonal selection principle proposed by Burnet, in the immune response process there is no crossover of genetic material between members of the repertoire, i.e., there is no knowledge communication during different elite pools in the previous clonal selection models. As a result, the search performance of these models is ineffective. To solve this problem, inspired by the concept of the idiotypic network theory, an expanded lateral interactive clonal selection algorithm (LICS) is put forward. In LICS, an antibody is matured not only through the somatic hypermutation and the receptor editing from the B cell, but also through the stimuli from other antibodies. The stimuli is realized by memorizing some common gene segment on the idiotypes, based on which a lateral interactive receptor editing operator is also introduced. Then, LICS is applied to several benchmark instances of the traveling salesman problem. Simulation results show the efficiency and robustness of LICS when compared to other traditional algorithms.

During the execution of software, tremendous amounts of data can be recorded. By exploiting the execution data, one can discover behavioral models to describe the actual software execution. As a well-known open-source process mining toolkit, ProM integrates quantities of process mining techniques and enjoys a variety of applications in a broad range of areas. How to develop a better ProM software, both from user experience and software performance perspective, are of vital importance. To achieve this goal, we need to investigate the real execution behavior of ProM which can provide useful insights on its usage and how it responds to user operations. This paper aims to propose an effective approach to solve this problem. To this end, we first instrument existing ProM framework to capture execution logs without changing its architecture. Then a two-layered framework is introduced to support accurate ProM behavior discovery by characterizing both user interaction behavior and plug-in calling behavior separately. Next, detailed discovery techniques to obtain user interaction behavior model and plug-in calling behavior model are proposed. All proposed approaches have been implemented.

Both the clonal selection algorithm (CSA) and the ant colony optimization (ACO) are inspired by natural phenomena and are effective tools for solving complex problems. CSA can exploit and explore the solution space parallely and effectively. However, it can not use enough environment feedback information and thus has to do a large redundancy repeat during search. On the other hand, ACO is based on the concept of indirect cooperative foraging process via secreting pheromones. Its positive feedback ability is nice but its convergence speed is slow because of the little initial pheromones. In this paper, we propose a pheromone-linker to combine these two algorithms. The proposed hybrid clonal selection and ant colony optimization (CSA-ACO) reasonably utilizes the superiorities of both algorithms and also overcomes their inherent disadvantages. Simulation results based on the traveling salesman problems have demonstrated the merit of the proposed algorithm over some traditional techniques.

Differential Evolution (DE) algorithm is simple and effective. Since DE has been proposed, it has been widely used to solve various complex optimization problems. To further exploit the advantages of DE, we propose a new variant of DE, termed as ranking-based differential evolution (RDE), by performing ranking on the population. Progressively better individuals in the population are used for mutation operation, thus improving the algorithm's exploitation and exploration capability. Experimental results on a number of benchmark optimization functions show that RDE significantly outperforms the original DE and performs competitively in comparison with other two state-of-the-art DE variants.

In this paper, we propose a stochastic dynamic local search (SDLS) method for Multiple-Valued Logic (MVL) learning by introducing stochastic dynamics into the traditional local search method. The proposed learning network maintains some trends of quick descent to either global minimum or a local minimum, and at the same time has some chance of escaping from local minima by permitting temporary error increases during learning. Thus the network may eventually reach the global minimum state or its best approximation with very high probability. Simulation results show that the proposed algorithm has the superior abilities to find the global minimum for the MVL network learning within reasonable number of iterations.

This paper presents an inertial estimator learning automata scheme by which both the short-term and long-term perspectives of the environment can be incorporated in the stochastic estimator – the long term information crystallized in terms of the running reward-probability estimates, and the short term information used by considering whether the most recent response was a reward or a penalty. Thus, when the short-term perspective is considered, the stochastic estimator becomes pertinent in the context of the estimator algorithms. The proposed automata employ an inertial weight estimator as the short-term perspective to achieve a rapid and accurate convergence when operating in stationary random environments. According to the proposed inertial estimator scheme, the estimates of the reward probabilities of actions are affected by the last response from environment. In this way, actions that have gotten the positive response from environment in the short time, have the opportunity to be estimated as “optimal”, to increase their choice probability and consequently, to be selected. The estimates become more reliable and consequently, the automaton rapidly and accurately converges to the optimal action. The asymptotic behavior of the proposed scheme is analyzed and it is proved to be ε-optimal in every stationary random environment. Extensive simulation results indicate that the proposed algorithm converges faster than the traditional stochastic-estimator-based S ERI scheme, and the deterministic-estimator-based DGPA and DPRI schemes when operating in stationary random environments.

The equilibrium optimizer (EO) is a novel physics-based meta-heuristic optimization algorithm that is inspired by estimating dynamics and equilibrium states in controlled volume mass balance models. As a stochastic optimization algorithm, EO inevitably produces duplicated solutions, which is wasteful of valuable evaluation opportunities. In addition, an excessive number of duplicated solutions can increase the risk of the algorithm getting trapped in local optima. In this paper, an improved EO algorithm with a bis-population-based non-revisiting (BNR) mechanism is proposed, namely BEO. It aims to eliminate duplicate solutions generated by the population during iterations, thus avoiding wasted evaluation opportunities. Furthermore, when a revisited solution is detected, the BNR mechanism activates its unique archive population learning mechanism to assist the algorithm in generating a high-quality solution using the excellent genes in the historical information, which not only improves the algorithm's population diversity but also helps the algorithm get out of the local optimum dilemma. Experimental findings with the IEEE CEC2017 benchmark demonstrate that the proposed BEO algorithm outperforms other seven representative meta-heuristic optimization techniques, including the original EO algorithm.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

The clonal selection algorithm (CS), inspired by the basic features of adaptive immune response to antigenic stimulus, can exploit and explore the solution space parallelly and effectively. However, antibody initialization and premature convergence are two problems of CS. To overcome these two problems, we propose a chaotic distance-based clonal selection algorithm (CDCS). In this novel algorithm, we introduce a chaotic initialization mechanism and a distance-based somatic hypermutation to improve the performance of CS. The proposed algorithm is also verified for numerous benchmark traveling salesman problems. Experimental results show that the improved algorithm proposed in this paper provides better performance when compared to other metaheuristics.

This paper represents a feedback artificial immune system (FAIS). Inspired by the feedback mechanisms in the biological immune system, the proposed algorithm effectively manipulates the population size by increasing and decreasing B cells according to the diversity of the current population. Two kinds of assessments are used to evaluate the diversity aiming to capture the characteristics of the problem on hand. Furthermore, the processing of adding and declining the number of population is designed. The validity of the proposed algorithm is tested for several traveling salesman benchmark problems. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the traditional genetic algorithm and an improved clonal selection algorithm.

This paper describes an improved local search method for synthesizing arbitrary Multiple-Valued Logic (MVL) function. In our approach, the MVL function is mapped from its algebraic presentation (sum-of-products form) on a multiple-layered network based on the functional completeness property. The output of the network is evaluated based on two metrics of correctness and optimality. A local search embedded with chaotic dynamics is utilized to train the network in order to minimize the MVL functions. With the characteristics of pseudo-randomness, ergodicity and irregularity, both the search sequence and solution neighbourhood generated by chaotic variables enables the system to avoid local minimum settling and improves the solution quality. Simulation results based on 2-variable 4-valued MVL functions and some other large instances also show that the improved local search learning algorithm outperforms the traditional methods in terms of the correctness and the average number of product terms required to realize a given MVL function.

In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.

The Clonal Selection Algorithm (CSA) is employed by the natural immune system to define the basic features of an immune response to an antigenic stimulus. In the immune response, according to Burnet's clonal selection principle, the antigen imposes a selective pressure on the antibody population by allowing only those cells which specifically recognize the antigen to be selected for proliferation and differentiation. However ongoing investigations indicate that receptor editing, which refers to the process whereby antigen receptor engagement leads to a secondary somatic gene rearrangement event and alteration of the receptor specificity, is occasionally found in affinity maturation process. In this paper, we extend the traditional CSA approach by incorporating the receptor editing method, named RECSA, and applying it to the Traveling Salesman Problem. Thus, both somatic hypermutation (HM) of clonal selection theory and receptor editing (RE) are utilized to improve antibody affinity. Simulation results and comparisons with other general algorithms show that the RECSA algorithm can effectively enhance the searching efficiency and greatly improve the searching quality within reasonable number of generations.

Business process similarity measure is required by many applications, such as business process query, improvement, redesign, and etc. Many process behavior similarity measures have been proposed in the past two decades. However, to the best of our knowledge, most existing work only focuses on the direct causality transition relations and totally neglect the concurrent and transitive transition relations that are proved to be equally important when measuring process behavior similarity. In this paper, we take the weakness of existing process behavior similarity measures as a starting point, and propose a comprehensive approach to measure the business process behavior similarity based on the so-called Extended Transition Relation set, ETR-set for short. Essentially, the ETR-set is an ex-tended transition relation set containing direct causal transition relations, minimum concurrent transition relations and transitive causal transition relations. Based on the ETR-set, a novel process behavior similarity measure is defined. By constructing a concurrent reachability graph, our approach finds an effective technique to obtain the ETR-set. Finally, we evaluate our proposed approach in terms of its property analysis as well as conducting a group of control experiments.

This paper presents a new multi-layered artificial immune system architecture using the ideas generated from the biological immune system for solving combinatorial optimization problems. The proposed methodology is composed of five layers. After expressing the problem as a suitable representation in the first layer, the search space and the features of the problem are estimated and extracted in the second and third layers, respectively. Through taking advantage of the minimized search space from estimation and the heuristic information from extraction, the antibodies (or solutions) are evolved in the fourth layer and finally the fittest antibody is exported. In order to demonstrate the efficiency of the proposed system, the graph planarization problem is tested. Simulation results based on several benchmark instances show that the proposed algorithm performs better than traditional algorithms.

Software watermarking is a digital technique used to protect software by embedding some secret information as identification in order to discourage software piracy and unauthorized modification. Watermarking is still a relatively new field and has good potential in protecting software from privacy threats. However, there appears to be a security vulnerability in the watermark trigger behaviour, and has been frequently attacked. By tracing the watermark trigger behaviour, attackers can easily intrude into the software and locate and expose the watermark for modification. In order to address this problem, we propose an algorithm that obscures the watermark trigger behaviour by utilizing buffer overflow. The code of the watermark trigger behaviour is removed from the software product itself, making it more difficult for attackers to trace the software. Experiments show that the new algorithm has promising performance in terms of the imperceptibility of software watermark.

This paper proposes a novel multiple chaos embedded gravitational search algorithm (MCGSA) that simultaneously utilizes multiple different chaotic maps with a manner of local search. The embedded chaotic local search can exploit a small region to refine solutions obtained by the canonical gravitational search algorithm (GSA) due to its inherent local exploitation ability. Meanwhile it also has a chance to explore a huge search space by taking advantages of the ergodicity of chaos. To fully utilize the dynamic properties of chaos, we propose three kinds of embedding strategies. The multiple chaotic maps are randomly, parallelly, or memory-selectively incorporated into GSA, respectively. To evaluate the effectiveness and efficiency of the proposed MCGSA, we compare it with GSA and twelve variants of chaotic GSA which use only a certain chaotic map on a set of 48 benchmark optimization functions. Experimental results show that MCGSA performs better than its competitors in terms of convergence speed and solution accuracy. In addition, statistical analysis based on Friedman test indicates that the parallelly embedding strategy is the most effective for improving the performance of GSA.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

This paper proposes a probabilistic modeling learning algorithm for the local search approach to the Multiple-Valued Logic (MVL) networks. The learning model (PMLS) has two phases: a local search (LS) phase, and a probabilistic modeling (PM) phase. The LS performs searches by updating the parameters of the MVL network. It is equivalent to a gradient decrease of the error measures, and leads to a local minimum of error that represents a good solution to the problem. Once the LS is trapped in local minima, the PM phase attempts to generate a new starting point for LS for further search. It is expected that the further search is guided to a promising area by the probability model. Thus, the proposed algorithm can escape from local minima and further search better results. We test the algorithm on many randomly generated MVL networks. Simulation results show that the proposed algorithm is better than the other improved local search learning methods, such as stochastic dynamic local search (SDLS) and chaotic dynamic local search (CDLS).