Special Issue "Swarm Intelligence Applications for NP Hard Challenges"
Deadline for manuscript submissions: closed (15 June 2020).
Interests: Artificial Intelligence; Swarm Intelligence; Digital Image Processing; Optimization Metaheuristics; Deep Learning
Interests: Swarm Intelligence; Digital Image Processing; Machine Learning
Interests: Artificial Intelligence; Swarm Intelligence; Digital Image Processing; Optimization Metaheuristics; Computer Networks
Interests: Artificial Intelligence; Swarm Intelligence; Optimization Metaheuristics; Computer Networks; Wireless Sensor Networks
Hard optimization problems that cannot be solved within acceptable computational time by deterministic mathematical methods have been successfully solved in recent years by population-based stochastic metaheuristics, among which swarm intelligence algorithms represent a prominent class. Swarm intelligence algorithms, along with evolutionary computation (EC) approaches, belong to a wider group of nature-inspired metaheuristics. Each nature-inspired method simulates some kind of natural phenomenon. For example, EC algorithms simulate the process of biological evolution, and one of the most significant EC algorithms, the genetic algorithm (GA), incorporates selection, crossover and mutation natural operators in its search process.
Swarm intelligence algorithms simulate organized and coordinated behaviour of groups of organisms, such as flock of birds, school of fish, colonies of ants, groups of bats, or herds of elephants. Despite the fact that the swarm consists of relatively unsophisticated individuals, swarm as a group exhibits intelligent behaviour by establishing direct and indirect forms of communication without the central component. In the literature, this characteristic is known as self-organization, and four basic principles of self-organization are positive and negative feed-back, multiple interactions and randomness.
Swarm intelligence metaheuristics conduct the search process by performing exploitation (intensification) and exploration (diversification). Exploitation and exploration meachnisms conduct local and global search, respectively. One of the most important challenges in the domain of swarm algorithms is to establish a proper balance adjustments (trade-off) between these two processes and many existing swarm approaches suffer from the inappropriate balance. If this balance is directed towards exploitation, the algorithm may suffer from the premature convergence and the optimal (or suboptimal) region of the search space may not be found. In the opposite case, the algorithm may find the optimal region, but could not perform a fine-tuned search around the promising solutions, which, as a consequence, may lead to a worse convergence.
In recent years, many hybrid swarm approaches combining the best features of two or more algorithms have been devised. For example, a hybrid algorithm may use an intensification equation from the first metaheuristics, and the diversification mechanism from the second approach. A number of hybridized swarm intelligence metaheuristics have been developed and implemented. Considering the fact that good hybrids are not created as a random combination of individual functional elements and procedures from diferent algorithms, but rather established on comprehensive analysis of the functional principles of the algorithms that are used in the process of hybridization, development of the hybrid approaches was preceded by thorough research of the advantages and disadvantages of each algorithm involved in order to determine the best combination that neutralizes disadvantages of one approach by incorporating the strengths of the other. However, when devising hybrid algorithms, according to the no free lunch theorem (NFLT), there always must be some kind of compromise and the researcher should be aware of this fact.
Some of the most prominent examples of state-of-the-art swarm algorithms include: particle swarm optimization (PSO), ant colony optimization (ACO), firefly algorithms (FA), bat algorithm (BA), artificial bee colony (ABC), fireworks algorithm (FWA), bacterial foraging optimization (BFO), elephant herding optimization (EHO), whale optimization algorithm (WAO), monarch butterfly optimization (MBO), brain storm optimization (BSO), etc. All these approaches in the original, modified/upgraded and hybridized versions have shown great potential when tackling many different types of NP real-world challenges.
Since the swarm approaches have proven to be robust optimizers of NP hard tasks, there is a logical assumption that many real-world NP hard challenges can be solved by using swarm intelligence algorithms in original, modified/upgraded and hybridized implementations. The most important goal of this Special Issue is to gather such research contributions.
However, despite the basic topic of this Special Issue, authors are also encouraged to submit manuscripts with theoretical discussion about the performance and behaviour of swarm approaches, as well as to present their EC applications to NP hard challenges.
Both original contributions and review articles will be considered, and we invite authors to submit their formal and technically sound manuscripts to cover (but not limited to) the following topics:
- Swarm Intelligence Applications in Engineering
- Swarm Intelligence Applications in Finance and Economics
- Swarm Intelligence Applications in Deep Learning
- Swarm Intelligence Applications in Wireless Sensor Networks (WSNs)
- Swarm Intelligence Applications in Cloud Computing
- Swarm Intelligence Applications in Internet of Things (IoT)
- Swarm Intelligence Applications in Smart Cities
- Swarm Intelligence Applications in Computer Vision and Image Processing
- Swarm Intelligence Applications in Crowdsourcing
- Swarm Intelligence Applications in Aerospace Science
- Swarm Intelligence Applications to Automatic Data Clustering and Analysis
- Swarm Intelligence Applications in Smart Logistics
- Swarm Intelligence Applications in Cybersecurity
- Hybrid Swarm Intelligence Algorithms
- Memtic Swarm Algorithms
- Parallel Swarm Algorithms
- Distributed Swarm Algorithms
- Swarm Intelligence Performance and Behaviour Analysis of Swarm algorithms
- Lage-scale Global Optimization
- Combinatorial Optimization
- Multi-objective Optimization
Dr. Nebojsa Bacanin
Dr. Eva Tuba
Dr. Milan Tuba
Dr. Ivana Strumberger
Manuscript Submission Information
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- Swarm intelligence metaheurisitcs
- Nature-inspired alogritthms
- Stohastic optimization
- EC algorithms
- Real-world NP hard problems
- Hybrid algorithms
- Memetic algorithms
- Parallel algorithms
- Global optimization
- Combinatorial optimization