Abstract
We propose a new multi-layer explosion strategy inspired by various explosion patterns of real reworks to accelerate reworks algorithm (FWA). Each rework individual conducts multiple explosions to explore a local fitness landscape carefully instead of a single layer explosion used in canonical FWA. In the proposal, each rework individual generates a small number of sparks in the first layer randomly, then the generated sparks conduct the second layer explosions to generate new diverse sparks. These new sparks repeat the above operations until the number of this iteration reaches the predefined maximum layer number. Theoretically, the number of explosion layers can be set to any positive integer, and the proposed strategy expects to generate various potential sparks using the multi-layer explosion strategy without changing the total number of generated sparks. The proposed strategy can combine with not only basic FWA but also other versions of FWA algorithms easily and replace their corresponding explosion operations to develop a new version, multi-layer explosion-based FWA. To evaluate the performance of our proposal, we select a more powerful variant of FWA, Enhanced FWA (EFWA) as the baseline algorithm and combine with our proposed explosion strategy. We run our proposal on 28 benchmark functions from CEC2013 test suites of 2-dimensions (2-D), 10-D and 30-D with 30 trial runs and compare with several state-of-theart EC algorithms. The experimental results confirm that the proposed strategy is effective and promising, which can obtain a better performance for FWA in terms of convergence speed and convergence accuracy. We finally analyze composition as well as feasibility of proposal and list some open topics.
Highlights
Swarm Intelligence algorithms are a kind of population-based optimization technique and mainly simulate the cooperation among simple individuals to achieve complex group behaviours
Some researchers developed other optimization algorithms inspired by natural phenomenon and human society, such as brain storm optimization algorithm [9], imperialist competitive algorithm [10] and others
Inspired by various explosion ways and shapes, we firstly introduce a different explosion model, multi-layer explosion to enhance the use of the local fitness landscape, while conventional fireworks algorithm (FWA) generates spark individuals around a firework individual at once
Summary
Swarm Intelligence algorithms are a kind of population-based optimization technique and mainly simulate the cooperation among simple individuals to achieve complex group behaviours. They have attracted many practitioners thanks to their outstanding characteristics, such as, easy-to-use, robustness, parallelism, intelligence and others. One of the representative swarm intelligence algorithms is Particle Swarm Optimization (PSO) [2] that simulates the foraging behaviour of birds to and the global optimum. Many efficient algorithms have been proposed, e.g. bacterial foraging optimization algorithm [3], artificial bee colony algorithm [4], cuckoo search [5], bat algorithm [6], krill herd [7], elephant herding optimization [8], and others. Some researchers developed other optimization algorithms inspired by natural phenomenon and human society, such as brain storm optimization algorithm [9], imperialist competitive algorithm [10] and others
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